🎯 Predykcja
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import os
import pandas as pd
import matplotlib.pyplot as plt
import os
import pandas as pd
import matplotlib.pyplot as plt
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# Folder where the CSV files are stored
input_folder = '../../data/scalone_dane'
# Initialize an empty list to store all data
all_data = []
# Dictionary to hold each city's DataFrame
city_dataframes = {}
# Loop through each CSV file in the folder
for filename in os.listdir(input_folder):
if filename.endswith('_scalony.csv'):
city = filename.replace('_scalony.csv', '') # Extract city name
file_path = os.path.join(input_folder, filename)
# Read CSV and add a 'CITY' column
df = pd.read_csv(file_path)
df['CITY'] = city
# Store individual city DataFrame in dictionary
city_dataframes[city] = df.copy()
# Add to the list for the combined DataFrame
all_data.append(df)
# Combine all into a single DataFrame
combined_df = pd.concat(all_data, ignore_index=True)
print(f"Loaded data from {len(all_data)} cities. Total rows: {len(combined_df)}")
# Folder where the CSV files are stored
input_folder = '../../data/scalone_dane'
# Initialize an empty list to store all data
all_data = []
# Dictionary to hold each city's DataFrame
city_dataframes = {}
# Loop through each CSV file in the folder
for filename in os.listdir(input_folder):
if filename.endswith('_scalony.csv'):
city = filename.replace('_scalony.csv', '') # Extract city name
file_path = os.path.join(input_folder, filename)
# Read CSV and add a 'CITY' column
df = pd.read_csv(file_path)
df['CITY'] = city
# Store individual city DataFrame in dictionary
city_dataframes[city] = df.copy()
# Add to the list for the combined DataFrame
all_data.append(df)
# Combine all into a single DataFrame
combined_df = pd.concat(all_data, ignore_index=True)
print(f"Loaded data from {len(all_data)} cities. Total rows: {len(combined_df)}")
Loaded data from 12 cities. Total rows: 218047
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# Generate separate plots for each city
for city, df in city_dataframes.items():
plt.figure(figsize=(12, 5))
plt.plot(df.index, df['TEMP'], label=f'Temperatura - {city}')
plt.title(f'Temperatura w czasie – {city}')
plt.xlabel('Data')
plt.ylabel('Temperatura')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
# Generate separate plots for each city
for city, df in city_dataframes.items():
plt.figure(figsize=(12, 5))
plt.plot(df.index, df['TEMP'], label=f'Temperatura - {city}')
plt.title(f'Temperatura w czasie – {city}')
plt.xlabel('Data')
plt.ylabel('Temperatura')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
# 1. Wczytanie danych
df = city_dataframes['Ateny'].copy()
# 2. Czyszczenie i przygotowanie
# Usunięcie kolumn typu "ATTRIBUTES", które są jakościowe i często puste
df = df[[col for col in df.columns if 'ATTRIBUTES' not in col]]
# Usunięcie kolumn niezmiennych lub zbędnych dla predykcji
df = df.drop(columns=['STATION', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'NAME', 'CITY'])
# Przekształcenie kolumny DATE do daty i dodanie cech czasowych
df['DATE'] = pd.to_datetime(df['DATE'], errors='coerce')
df['Year'] = df['DATE'].dt.year
df['Month'] = df['DATE'].dt.month
df['Day'] = df['DATE'].dt.day
df = df.drop(columns=['DATE'])
# Usunięcie wierszy z brakami danych
df = df.dropna()
# 3. Korelacja z celem (TEMP)
correlations = df.corr(numeric_only=True)['TEMP'].sort_values(ascending=False)
# Wyświetlenie korelacji
print("Korelacje z TEMP:")
print(correlations)
# Wykres korelacji
plt.figure(figsize=(10, 6))
sns.barplot(x=correlations.values, y=correlations.index)
plt.title("Korelacja cech z temperaturą (TEMP)")
plt.xlabel("Współczynnik korelacji")
plt.ylabel("Cechy")
plt.tight_layout()
plt.show()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
# 1. Wczytanie danych
df = city_dataframes['Ateny'].copy()
# 2. Czyszczenie i przygotowanie
# Usunięcie kolumn typu "ATTRIBUTES", które są jakościowe i często puste
df = df[[col for col in df.columns if 'ATTRIBUTES' not in col]]
# Usunięcie kolumn niezmiennych lub zbędnych dla predykcji
df = df.drop(columns=['STATION', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'NAME', 'CITY'])
# Przekształcenie kolumny DATE do daty i dodanie cech czasowych
df['DATE'] = pd.to_datetime(df['DATE'], errors='coerce')
df['Year'] = df['DATE'].dt.year
df['Month'] = df['DATE'].dt.month
df['Day'] = df['DATE'].dt.day
df = df.drop(columns=['DATE'])
# Usunięcie wierszy z brakami danych
df = df.dropna()
# 3. Korelacja z celem (TEMP)
correlations = df.corr(numeric_only=True)['TEMP'].sort_values(ascending=False)
# Wyświetlenie korelacji
print("Korelacje z TEMP:")
print(correlations)
# Wykres korelacji
plt.figure(figsize=(10, 6))
sns.barplot(x=correlations.values, y=correlations.index)
plt.title("Korelacja cech z temperaturą (TEMP)")
plt.xlabel("Współczynnik korelacji")
plt.ylabel("Cechy")
plt.tight_layout()
plt.show()
Korelacje z TEMP: TEMP 1.000000 MAX 0.977828 MIN 0.947702 DEWP 0.808331 Month 0.293717 MXSPD 0.145355 VISIB 0.144031 WDSP 0.103878 Year 0.044437 Day 0.009142 GUST -0.076934 FRSHTT -0.174427 PRCP -0.323678 SLP NaN STP NaN SNDP NaN Name: TEMP, dtype: float64
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from statsmodels.stats.outliers_influence import variance_inflation_factor
from statsmodels.tools.tools import add_constant
# Zakładamy, że df już nie zawiera TEMP
features = df.drop(columns=['TEMP']).copy()
# Dodaj stałą (bias term) potrzebną do obliczenia VIF
features_with_const = add_constant(features)
# Obliczanie VIF
vif_data = pd.DataFrame()
vif_data["Feature"] = features_with_const.columns
vif_data["VIF"] = [variance_inflation_factor(features_with_const.values, i)
for i in range(features_with_const.shape[1])]
print("\n📊 Współczynniki VIF (Variance Inflation Factor):")
print(vif_data.sort_values(by="VIF", ascending=False))
from statsmodels.stats.outliers_influence import variance_inflation_factor
from statsmodels.tools.tools import add_constant
# Zakładamy, że df już nie zawiera TEMP
features = df.drop(columns=['TEMP']).copy()
# Dodaj stałą (bias term) potrzebną do obliczenia VIF
features_with_const = add_constant(features)
# Obliczanie VIF
vif_data = pd.DataFrame()
vif_data["Feature"] = features_with_const.columns
vif_data["VIF"] = [variance_inflation_factor(features_with_const.values, i)
for i in range(features_with_const.shape[1])]
print("\n📊 Współczynniki VIF (Variance Inflation Factor):")
print(vif_data.sort_values(by="VIF", ascending=False))
📊 Współczynniki VIF (Variance Inflation Factor): Feature VIF 8 MIN 10.307664 7 MAX 8.578652 4 WDSP 8.184609 5 MXSPD 6.237028 0 DEWP 4.311201 6 GUST 1.815506 9 PRCP 1.637067 11 FRSHTT 1.269011 13 Month 1.189234 3 VISIB 1.039370 12 Year 1.019603 14 Day 1.001919 10 SNDP 0.000026 2 STP 0.000006 1 SLP 0.000000
C:\Users\mikos\AppData\Local\Programs\Python\Python312\Lib\site-packages\statsmodels\regression\linear_model.py:1782: RuntimeWarning: divide by zero encountered in scalar divide return 1 - self.ssr/self.centered_tss
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selected_features = ['DEWP', 'Month', 'PRCP', 'VISIB']
selected_features = ['DEWP', 'Month', 'PRCP', 'VISIB']
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import numpy as np
from statsmodels.tsa.statespace.sarimax import SARIMAX
from sklearn.metrics import mean_squared_error, mean_absolute_error
import warnings
import pandas as pd
from itertools import product
import seaborn as sns
from statsmodels.tsa.stattools import adfuller
from tqdm import tqdm
import numpy as np
from statsmodels.tsa.statespace.sarimax import SARIMAX
from sklearn.metrics import mean_squared_error, mean_absolute_error
import warnings
import pandas as pd
from itertools import product
import seaborn as sns
from statsmodels.tsa.stattools import adfuller
from tqdm import tqdm
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warnings.filterwarnings("ignore")
warnings.filterwarnings("ignore")
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results_summary = []
# Parametry
exog_vars = ['DEWP', 'Month', 'PRCP', 'VISIB']
forecast_horizon = 1
test_size_ratio = 0.10
validation_size_ratio = 0.15
last_n_years = 2
results_summary = []
# Parametry
exog_vars = ['DEWP', 'Month', 'PRCP', 'VISIB']
forecast_horizon = 1
test_size_ratio = 0.10
validation_size_ratio = 0.15
last_n_years = 2
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seasonal_order = (1, 1, 1, 365)
# plot_path = f"plots/{city}__sarimax_plot.png"
forecast_results = []
for city, df in city_dataframes.items():
plot_path = f"../../plots/{city}__sarimax_plot.png"
data = df.copy()
# Filtrujemy tylko ostatnie 2 lata danych
data.reset_index(inplace=True)
data['DATE'] = pd.to_datetime(data['DATE'], errors='coerce')
data['Month'] = data['DATE'].dt.month
data.set_index('DATE', inplace=True)
last_date = data.index.max()
cutoff_date = last_date - pd.DateOffset(years=last_n_years)
data = data[data.index >= cutoff_date]
data[exog_vars + ['TEMP']] = data[exog_vars + ['TEMP']].apply(pd.to_numeric, errors='coerce')
data = data.dropna(subset=['TEMP'] + exog_vars)
n_total = len(data)
n_test = int(test_size_ratio * n_total)
n_val = int(validation_size_ratio * (n_total - n_test))
n_train = n_total - n_val - n_test
train = data.iloc[:n_train]
val = data.iloc[n_train:n_train + n_val]
test = data.iloc[n_train + n_val:]
combined_train = pd.concat([train, val])
best_aicc = np.inf
best_order = (0, 0, 0)
# p = d = q = range(3)
# candidate_orders = list(product(p, d, q))
candidate_orders = list(product(range(3), repeat=3)) # or range(5)
for order in tqdm(candidate_orders, desc=f"{city} model selection"):
print(order)
try:
model = SARIMAX(combined_train['TEMP'], exog=combined_train[exog_vars], order=order, #seasonal_order=(1,1,1,365),
enforce_stationarity=False, enforce_invertibility=False)
print('2')
res = model.fit(disp=True)
print('1')
aicc = res.aic + (2 * len(res.params)**2 + 2 * len(res.params)) / (len(train) - len(res.params) - 1)
residuals = res.resid
# print('1')
adf_result = adfuller(residuals.dropna())
# print('1')
p_value = adf_result[1]
if p_value < 0.05 and aicc < best_aicc:
best_aicc = aicc
best_order = order
print(f"Order: {order}, AICc: {aicc:.2f}, ADF p-value: {p_value:.4f}, Best_Order: {best_order}, Best_aicc: {best_aicc:.2f}")
except:
continue
# Rolling forecast: iteracyjne prognozowanie 1 kroku do przodu
history_y = combined_train['TEMP'].tolist()
history_exog = combined_train[exog_vars].copy()
predictions, lowers, uppers, actuals = [], [], [], []
for t in tqdm(range(len(test)), desc=f"Analysed test_samples from: "):
try:
print('1')
model = SARIMAX(history_y, exog=history_exog, order=best_order, #seasonal_order=(1,1,1,365),
enforce_stationarity=False, enforce_invertibility=False)
model_fit = model.fit(disp=False)
next_exog = test[exog_vars].iloc[[t]]
pred = model_fit.get_forecast(steps=1, exog=next_exog)
pred_mean = pred.predicted_mean.iloc[0]
ci = pred.conf_int(alpha=0.05).iloc[0]
print(pred_mean)
predictions.append(pred_mean)
lowers.append(ci[0])
uppers.append(ci[1])
actuals.append(test['TEMP'].iloc[t])
if not np.isnan(pred_mean):
history_y.append(pred_mean)
history_exog = pd.concat([history_exog, next_exog])
else:
print(f"⚠️ Skipped step {t} due to NaN prediction.")
continue
# history_y.append(pred_mean)
# history_exog = pd.concat([history_exog, next_exog])
except:
predictions.append(np.nan)
lowers.append(np.nan)
uppers.append(np.nan)
actuals.append(test['TEMP'].iloc[t])
# Ewaluacja
y_true = np.array(actuals)
y_pred = np.array(predictions)
print(np.isnan(y_true).any())
print(np.isnan(y_pred).any())
mae = mean_absolute_error(y_true, y_pred)
rmse = np.sqrt(mean_squared_error(y_true, y_pred))
forecast_results.append({
'City': city,
'Best Order': best_order,
'Test MAE': mae,
'Test RMSE': rmse,
'AICc': best_aicc
})
# Wykres
plt.figure(figsize=(15, 6))
plt.plot(data.index[:n_train], data['TEMP'].iloc[:n_train], label='Train', color='blue')
plt.plot(data.index[n_train:n_train+n_val], data['TEMP'].iloc[n_train:n_train+n_val], label='Val', color='orange')
plt.plot(data.index[n_train+n_val:], actuals, label='Test Actual', color='green')
plt.plot(data.index[n_train+n_val:], predictions, label='Forecast', color='red')
plt.fill_between(data.index[n_train+n_val:], lowers, uppers, color='red', alpha=0.3, label='95% CI')
plt.title(f"SARIMAX Forecast with 95% CI — Order {best_order}")
plt.xlabel("Date")
plt.ylabel("Temperature")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.savefig(plot_path)
plt.show()
seasonal_order = (1, 1, 1, 365)
# plot_path = f"plots/{city}__sarimax_plot.png"
forecast_results = []
for city, df in city_dataframes.items():
plot_path = f"../../plots/{city}__sarimax_plot.png"
data = df.copy()
# Filtrujemy tylko ostatnie 2 lata danych
data.reset_index(inplace=True)
data['DATE'] = pd.to_datetime(data['DATE'], errors='coerce')
data['Month'] = data['DATE'].dt.month
data.set_index('DATE', inplace=True)
last_date = data.index.max()
cutoff_date = last_date - pd.DateOffset(years=last_n_years)
data = data[data.index >= cutoff_date]
data[exog_vars + ['TEMP']] = data[exog_vars + ['TEMP']].apply(pd.to_numeric, errors='coerce')
data = data.dropna(subset=['TEMP'] + exog_vars)
n_total = len(data)
n_test = int(test_size_ratio * n_total)
n_val = int(validation_size_ratio * (n_total - n_test))
n_train = n_total - n_val - n_test
train = data.iloc[:n_train]
val = data.iloc[n_train:n_train + n_val]
test = data.iloc[n_train + n_val:]
combined_train = pd.concat([train, val])
best_aicc = np.inf
best_order = (0, 0, 0)
# p = d = q = range(3)
# candidate_orders = list(product(p, d, q))
candidate_orders = list(product(range(3), repeat=3)) # or range(5)
for order in tqdm(candidate_orders, desc=f"{city} model selection"):
print(order)
try:
model = SARIMAX(combined_train['TEMP'], exog=combined_train[exog_vars], order=order, #seasonal_order=(1,1,1,365),
enforce_stationarity=False, enforce_invertibility=False)
print('2')
res = model.fit(disp=True)
print('1')
aicc = res.aic + (2 * len(res.params)**2 + 2 * len(res.params)) / (len(train) - len(res.params) - 1)
residuals = res.resid
# print('1')
adf_result = adfuller(residuals.dropna())
# print('1')
p_value = adf_result[1]
if p_value < 0.05 and aicc < best_aicc:
best_aicc = aicc
best_order = order
print(f"Order: {order}, AICc: {aicc:.2f}, ADF p-value: {p_value:.4f}, Best_Order: {best_order}, Best_aicc: {best_aicc:.2f}")
except:
continue
# Rolling forecast: iteracyjne prognozowanie 1 kroku do przodu
history_y = combined_train['TEMP'].tolist()
history_exog = combined_train[exog_vars].copy()
predictions, lowers, uppers, actuals = [], [], [], []
for t in tqdm(range(len(test)), desc=f"Analysed test_samples from: "):
try:
print('1')
model = SARIMAX(history_y, exog=history_exog, order=best_order, #seasonal_order=(1,1,1,365),
enforce_stationarity=False, enforce_invertibility=False)
model_fit = model.fit(disp=False)
next_exog = test[exog_vars].iloc[[t]]
pred = model_fit.get_forecast(steps=1, exog=next_exog)
pred_mean = pred.predicted_mean.iloc[0]
ci = pred.conf_int(alpha=0.05).iloc[0]
print(pred_mean)
predictions.append(pred_mean)
lowers.append(ci[0])
uppers.append(ci[1])
actuals.append(test['TEMP'].iloc[t])
if not np.isnan(pred_mean):
history_y.append(pred_mean)
history_exog = pd.concat([history_exog, next_exog])
else:
print(f"⚠️ Skipped step {t} due to NaN prediction.")
continue
# history_y.append(pred_mean)
# history_exog = pd.concat([history_exog, next_exog])
except:
predictions.append(np.nan)
lowers.append(np.nan)
uppers.append(np.nan)
actuals.append(test['TEMP'].iloc[t])
# Ewaluacja
y_true = np.array(actuals)
y_pred = np.array(predictions)
print(np.isnan(y_true).any())
print(np.isnan(y_pred).any())
mae = mean_absolute_error(y_true, y_pred)
rmse = np.sqrt(mean_squared_error(y_true, y_pred))
forecast_results.append({
'City': city,
'Best Order': best_order,
'Test MAE': mae,
'Test RMSE': rmse,
'AICc': best_aicc
})
# Wykres
plt.figure(figsize=(15, 6))
plt.plot(data.index[:n_train], data['TEMP'].iloc[:n_train], label='Train', color='blue')
plt.plot(data.index[n_train:n_train+n_val], data['TEMP'].iloc[n_train:n_train+n_val], label='Val', color='orange')
plt.plot(data.index[n_train+n_val:], actuals, label='Test Actual', color='green')
plt.plot(data.index[n_train+n_val:], predictions, label='Forecast', color='red')
plt.fill_between(data.index[n_train+n_val:], lowers, uppers, color='red', alpha=0.3, label='95% CI')
plt.title(f"SARIMAX Forecast with 95% CI — Order {best_order}")
plt.xlabel("Date")
plt.ylabel("Temperature")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.savefig(plot_path)
plt.show()
Ateny model selection: 0%| | 0/27 [00:00<?, ?it/s]
(0, 0, 0) 2
Ateny model selection: 4%|██▏ | 1/27 [00:00<00:07, 3.43it/s]
1 Order: (0, 0, 0), AICc: 4396.07, ADF p-value: 0.0241, Best_Order: (0, 0, 0), Best_aicc: 4396.07 (0, 0, 1) 2
Ateny model selection: 7%|████▍ | 2/27 [00:00<00:08, 2.90it/s]
1 Order: (0, 0, 1), AICc: 3953.59, ADF p-value: 0.0857, Best_Order: (0, 0, 0), Best_aicc: 4396.07 (0, 0, 2) 2
Ateny model selection: 11%|██████▋ | 3/27 [00:01<00:09, 2.57it/s]
1 Order: (0, 0, 2), AICc: 3728.44, ADF p-value: 0.2754, Best_Order: (0, 0, 0), Best_aicc: 4396.07 (0, 1, 0) 2
Ateny model selection: 15%|████████▉ | 4/27 [00:01<00:08, 2.84it/s]
1 Order: (0, 1, 0), AICc: 3105.77, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3105.77 (0, 1, 1) 2
Ateny model selection: 19%|███████████ | 5/27 [00:01<00:07, 3.04it/s]
1 Order: (0, 1, 1), AICc: 3102.88, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3102.88 (0, 1, 2) 2
Ateny model selection: 22%|█████████████▎ | 6/27 [00:02<00:07, 2.84it/s]
1 Order: (0, 1, 2), AICc: 3086.43, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43 (0, 2, 0) 2
Ateny model selection: 26%|███████████████▌ | 7/27 [00:02<00:06, 3.01it/s]
1 Order: (0, 2, 0), AICc: 3554.22, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43 (0, 2, 1) 2
Ateny model selection: 30%|█████████████████▊ | 8/27 [00:02<00:07, 2.63it/s]
1 Order: (0, 2, 1), AICc: 3104.95, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43 (0, 2, 2) 2
Ateny model selection: 33%|████████████████████ | 9/27 [00:03<00:07, 2.30it/s]
1 Order: (0, 2, 2), AICc: 3103.72, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43 (1, 0, 0) 2
Ateny model selection: 37%|█████████████████████▊ | 10/27 [00:03<00:07, 2.40it/s]
1 Order: (1, 0, 0), AICc: 3112.79, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43 (1, 0, 1) 2
Ateny model selection: 41%|████████████████████████ | 11/27 [00:04<00:06, 2.29it/s]
1 Order: (1, 0, 1), AICc: 3109.69, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43 (1, 0, 2) 2
Ateny model selection: 44%|██████████████████████████▏ | 12/27 [00:04<00:07, 2.08it/s]
1 Order: (1, 0, 2), AICc: 3092.68, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43 (1, 1, 0) 2
Ateny model selection: 48%|████████████████████████████▍ | 13/27 [00:05<00:05, 2.45it/s]
1 Order: (1, 1, 0), AICc: 3107.56, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43 (1, 1, 1) 2
Ateny model selection: 52%|██████████████████████████████▌ | 14/27 [00:05<00:05, 2.55it/s]
1 Order: (1, 1, 1), AICc: 3074.64, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 3074.64 (1, 1, 2) 2
Ateny model selection: 56%|████████████████████████████████▊ | 15/27 [00:05<00:04, 2.49it/s]
1 Order: (1, 1, 2), AICc: 3065.88, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88 (1, 2, 0) 2
Ateny model selection: 59%|██████████████████████████████████▉ | 16/27 [00:06<00:03, 2.87it/s]
1 Order: (1, 2, 0), AICc: 3416.84, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88 (1, 2, 1) 2
Ateny model selection: 63%|█████████████████████████████████████▏ | 17/27 [00:06<00:04, 2.43it/s]
1 Order: (1, 2, 1), AICc: 3106.91, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88 (1, 2, 2) 2
Ateny model selection: 67%|███████████████████████████████████████▎ | 18/27 [00:07<00:04, 2.09it/s]
1 Order: (1, 2, 2), AICc: 3104.79, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88 (2, 0, 0) 2
Ateny model selection: 70%|█████████████████████████████████████████▌ | 19/27 [00:07<00:03, 2.10it/s]
1 Order: (2, 0, 0), AICc: 3109.98, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88 (2, 0, 1) 2
Ateny model selection: 74%|███████████████████████████████████████████▋ | 20/27 [00:08<00:03, 1.75it/s]
1 Order: (2, 0, 1), AICc: 3114.78, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88 (2, 0, 2) 2
Ateny model selection: 78%|█████████████████████████████████████████████▉ | 21/27 [00:09<00:03, 1.63it/s]
1 Order: (2, 0, 2), AICc: 3074.17, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88 (2, 1, 0) 2
Ateny model selection: 81%|████████████████████████████████████████████████ | 22/27 [00:09<00:02, 1.94it/s]
1 Order: (2, 1, 0), AICc: 3095.69, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88 (2, 1, 1) 2
Ateny model selection: 85%|██████████████████████████████████████████████████▎ | 23/27 [00:09<00:01, 2.04it/s]
1 Order: (2, 1, 1), AICc: 3070.17, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88 (2, 1, 2) 2
Ateny model selection: 89%|████████████████████████████████████████████████████▍ | 24/27 [00:10<00:01, 1.90it/s]
1 Order: (2, 1, 2), AICc: 3067.51, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88 (2, 2, 0) 2
Ateny model selection: 93%|██████████████████████████████████████████████████████▋ | 25/27 [00:10<00:00, 2.22it/s]
1 Order: (2, 2, 0), AICc: 3340.22, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88 (2, 2, 1) 2
Ateny model selection: 96%|████████████████████████████████████████████████████████▊ | 26/27 [00:11<00:00, 2.02it/s]
1 Order: (2, 2, 1), AICc: 3100.73, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88 (2, 2, 2) 2
Ateny model selection: 100%|███████████████████████████████████████████████████████████| 27/27 [00:12<00:00, 2.21it/s]
1 Order: (2, 2, 2), AICc: 3101.37, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
Analysed test_samples from: 0%| | 0/73 [00:00<?, ?it/s]
1
Analysed test_samples from: 1%|▊ | 1/73 [00:00<00:30, 2.32it/s]
67.30751577723834 1
Analysed test_samples from: 3%|█▌ | 2/73 [00:00<00:34, 2.06it/s]
67.58020612561286 1
Analysed test_samples from: 4%|██▎ | 3/73 [00:01<00:34, 2.04it/s]
68.31755407971727 1
Analysed test_samples from: 5%|███ | 4/73 [00:01<00:35, 1.96it/s]
67.71570252700727 1
Analysed test_samples from: 7%|███▊ | 5/73 [00:02<00:37, 1.82it/s]
66.17883403048066 1
Analysed test_samples from: 8%|████▌ | 6/73 [00:02<00:32, 2.06it/s]
68.48956912067754 1
Analysed test_samples from: 10%|█████▎ | 7/73 [00:03<00:33, 1.99it/s]
68.80234455582575 1
Analysed test_samples from: 11%|██████ | 8/73 [00:03<00:32, 2.03it/s]
68.38275482900781 1
Analysed test_samples from: 12%|██████▊ | 9/73 [00:04<00:32, 1.94it/s]
67.81336824804666 1
Analysed test_samples from: 14%|███████▍ | 10/73 [00:05<00:31, 1.98it/s]
68.25288479270066 1
Analysed test_samples from: 15%|████████▏ | 11/73 [00:05<00:29, 2.08it/s]
67.56452565233826 1
Analysed test_samples from: 16%|████████▉ | 12/73 [00:05<00:28, 2.15it/s]
67.30627584721718 1
Analysed test_samples from: 18%|█████████▌ | 13/73 [00:06<00:26, 2.25it/s]
67.67133095384028 1
Analysed test_samples from: 19%|██████████▎ | 14/73 [00:06<00:24, 2.36it/s]
65.78528705259637 1
Analysed test_samples from: 21%|███████████ | 15/73 [00:07<00:23, 2.44it/s]
67.96213960281582 1
Analysed test_samples from: 22%|███████████▊ | 16/73 [00:07<00:23, 2.47it/s]
67.27325770808953 1
Analysed test_samples from: 23%|████████████▌ | 17/73 [00:07<00:22, 2.50it/s]
65.82157259668278 1
Analysed test_samples from: 25%|█████████████▎ | 18/73 [00:08<00:21, 2.55it/s]
66.34243611646295 1
Analysed test_samples from: 26%|██████████████ | 19/73 [00:08<00:21, 2.53it/s]
66.40037772512548 1
Analysed test_samples from: 27%|██████████████▊ | 20/73 [00:09<00:22, 2.38it/s]
66.83440847601885 1
Analysed test_samples from: 29%|███████████████▌ | 21/73 [00:09<00:22, 2.28it/s]
66.08218886529023 1
Analysed test_samples from: 30%|████████████████▎ | 22/73 [00:10<00:22, 2.24it/s]
64.63174805072623 1
Analysed test_samples from: 32%|█████████████████ | 23/73 [00:10<00:22, 2.22it/s]
66.97916023130715 1
Analysed test_samples from: 33%|█████████████████▊ | 24/73 [00:11<00:24, 1.99it/s]
65.04060288208066 1
Analysed test_samples from: 34%|██████████████████▍ | 25/73 [00:11<00:22, 2.13it/s]
65.00529121228288 1
Analysed test_samples from: 36%|███████████████████▏ | 26/73 [00:11<00:20, 2.27it/s]
65.70606836510586 1
Analysed test_samples from: 37%|███████████████████▉ | 27/73 [00:12<00:20, 2.28it/s]
66.54517953294632 1
Analysed test_samples from: 38%|████████████████████▋ | 28/73 [00:12<00:19, 2.37it/s]
64.86704240371732 1
Analysed test_samples from: 40%|█████████████████████▍ | 29/73 [00:13<00:18, 2.39it/s]
65.64823530677515 1
Analysed test_samples from: 41%|██████████████████████▏ | 30/73 [00:13<00:17, 2.42it/s]
62.08961238283027 1
Analysed test_samples from: 42%|██████████████████████▉ | 31/73 [00:13<00:17, 2.46it/s]
58.53074044059675 1
Analysed test_samples from: 44%|███████████████████████▋ | 32/73 [00:14<00:17, 2.29it/s]
61.77122689343072 1
Analysed test_samples from: 45%|████████████████████████▍ | 33/73 [00:14<00:17, 2.33it/s]
64.75093615990308 1
Analysed test_samples from: 47%|█████████████████████████▏ | 34/73 [00:15<00:16, 2.38it/s]
65.70699137334591 1
Analysed test_samples from: 48%|█████████████████████████▉ | 35/73 [00:15<00:15, 2.39it/s]
65.99137159058182 1
Analysed test_samples from: 49%|██████████████████████████▋ | 36/73 [00:16<00:16, 2.27it/s]
66.48732654673779 1
Analysed test_samples from: 51%|███████████████████████████▎ | 37/73 [00:16<00:15, 2.40it/s]
63.64802993558176 1
Analysed test_samples from: 52%|████████████████████████████ | 38/73 [00:16<00:14, 2.44it/s]
58.53051394074206 1
Analysed test_samples from: 53%|████████████████████████████▊ | 39/73 [00:17<00:14, 2.42it/s]
58.9645033430087 1
Analysed test_samples from: 55%|█████████████████████████████▌ | 40/73 [00:17<00:13, 2.37it/s]
63.67447423538126 1
Analysed test_samples from: 56%|██████████████████████████████▎ | 41/73 [00:18<00:13, 2.35it/s]
64.94752309384153 1
Analysed test_samples from: 58%|███████████████████████████████ | 42/73 [00:18<00:13, 2.33it/s]
63.96979628708432 1
Analysed test_samples from: 59%|███████████████████████████████▊ | 43/73 [00:18<00:12, 2.46it/s]
65.26176497183258 1
Analysed test_samples from: 60%|████████████████████████████████▌ | 44/73 [00:19<00:12, 2.25it/s]
67.40231219649549 1
Analysed test_samples from: 62%|█████████████████████████████████▎ | 45/73 [00:20<00:13, 2.08it/s]
67.69178859070107 1
Analysed test_samples from: 63%|██████████████████████████████████ | 46/73 [00:20<00:13, 1.96it/s]
64.71074593261602 1
Analysed test_samples from: 64%|██████████████████████████████████▊ | 47/73 [00:21<00:15, 1.72it/s]
63.985032008282914 1
Analysed test_samples from: 66%|███████████████████████████████████▌ | 48/73 [00:21<00:14, 1.75it/s]
63.519544848370714 1
Analysed test_samples from: 67%|████████████████████████████████████▏ | 49/73 [00:22<00:14, 1.67it/s]
63.699805639191375 1
Analysed test_samples from: 68%|████████████████████████████████████▉ | 50/73 [00:23<00:12, 1.82it/s]
61.7034416570571 1
Analysed test_samples from: 70%|█████████████████████████████████████▋ | 51/73 [00:23<00:11, 1.98it/s]
61.147674854925796 1
Analysed test_samples from: 71%|██████████████████████████████████████▍ | 52/73 [00:23<00:10, 2.06it/s]
61.81305471519303 1
Analysed test_samples from: 73%|███████████████████████████████████████▏ | 53/73 [00:24<00:09, 2.17it/s]
63.49743644566179 1
Analysed test_samples from: 74%|███████████████████████████████████████▉ | 54/73 [00:24<00:08, 2.16it/s]
63.584195487951185 1
Analysed test_samples from: 75%|████████████████████████████████████████▋ | 55/73 [00:25<00:08, 2.02it/s]
64.62593132939047 1
Analysed test_samples from: 77%|█████████████████████████████████████████▍ | 56/73 [00:25<00:08, 1.92it/s]
67.14326687269809 1
Analysed test_samples from: 78%|██████████████████████████████████████████▏ | 57/73 [00:26<00:08, 1.86it/s]
66.1242458767255 1
Analysed test_samples from: 79%|██████████████████████████████████████████▉ | 58/73 [00:26<00:07, 2.04it/s]
63.114988492319 1
Analysed test_samples from: 81%|███████████████████████████████████████████▋ | 59/73 [00:27<00:06, 2.17it/s]
61.7034598620619 1
Analysed test_samples from: 82%|████████████████████████████████████████████▍ | 60/73 [00:27<00:05, 2.25it/s]
60.198793806645995 1
Analysed test_samples from: 84%|█████████████████████████████████████████████ | 61/73 [00:28<00:05, 2.37it/s]
61.24019609502166 1
Analysed test_samples from: 85%|█████████████████████████████████████████████▊ | 62/73 [00:28<00:04, 2.38it/s]
62.075689595452886 1
Analysed test_samples from: 86%|██████████████████████████████████████████████▌ | 63/73 [00:28<00:04, 2.46it/s]
62.94374699365771 1
Analysed test_samples from: 88%|███████████████████████████████████████████████▎ | 64/73 [00:29<00:03, 2.34it/s]
63.58416893219419 1
Analysed test_samples from: 89%|████████████████████████████████████████████████ | 65/73 [00:29<00:03, 2.30it/s]
62.195130834805695 1
Analysed test_samples from: 90%|████████████████████████████████████████████████▊ | 66/73 [00:30<00:03, 1.96it/s]
63.96029180606662 1
Analysed test_samples from: 92%|█████████████████████████████████████████████████▌ | 67/73 [00:30<00:02, 2.18it/s]
63.67101684214818 1
Analysed test_samples from: 93%|██████████████████████████████████████████████████▎ | 68/73 [00:31<00:02, 2.35it/s]
64.56797200836436 1
Analysed test_samples from: 95%|███████████████████████████████████████████████████ | 69/73 [00:31<00:01, 2.44it/s]
63.98924072967211 1
Analysed test_samples from: 96%|███████████████████████████████████████████████████▊ | 70/73 [00:31<00:01, 2.39it/s]
63.15006552029184 1
Analysed test_samples from: 97%|████████████████████████████████████████████████████▌ | 71/73 [00:32<00:00, 2.47it/s]
63.3526093949729 1
Analysed test_samples from: 99%|█████████████████████████████████████████████████████▎| 72/73 [00:32<00:00, 2.43it/s]
63.728780803865625 1
Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:33<00:00, 2.20it/s]
63.69984444235352 False False
Berlin model selection: 0%| | 0/27 [00:00<?, ?it/s]
(0, 0, 0) 2
Berlin model selection: 4%|██▏ | 1/27 [00:00<00:06, 4.17it/s]
1 Order: (0, 0, 0), AICc: 4056.35, ADF p-value: 0.0117, Best_Order: (0, 0, 0), Best_aicc: 4056.35 (0, 0, 1) 2
Berlin model selection: 7%|████▎ | 2/27 [00:00<00:06, 3.60it/s]
1 Order: (0, 0, 1), AICc: 3825.78, ADF p-value: 0.0136, Best_Order: (0, 0, 1), Best_aicc: 3825.78 (0, 0, 2) 2
Berlin model selection: 11%|██████▌ | 3/27 [00:00<00:08, 2.89it/s]
1 Order: (0, 0, 2), AICc: 3740.32, ADF p-value: 0.0000, Best_Order: (0, 0, 2), Best_aicc: 3740.32 (0, 1, 0) 2
Berlin model selection: 19%|██████████▉ | 5/27 [00:01<00:05, 4.00it/s]
1 Order: (0, 1, 0), AICc: 3460.40, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3460.40 (0, 1, 1) 2 1 Order: (0, 1, 1), AICc: 3435.67, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3435.67 (0, 1, 2) 2
Berlin model selection: 22%|█████████████ | 6/27 [00:01<00:05, 3.62it/s]
1 Order: (0, 1, 2), AICc: 3401.93, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93 (0, 2, 0) 2
Berlin model selection: 26%|███████████████▎ | 7/27 [00:01<00:05, 3.88it/s]
1 Order: (0, 2, 0), AICc: 3956.69, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93 (0, 2, 1) 2
Berlin model selection: 30%|█████████████████▍ | 8/27 [00:02<00:05, 3.59it/s]
1 Order: (0, 2, 1), AICc: 3453.62, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93 (0, 2, 2) 2
Berlin model selection: 33%|███████████████████▋ | 9/27 [00:02<00:05, 3.13it/s]
1 Order: (0, 2, 2), AICc: 3438.31, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93 (1, 0, 0) 2
Berlin model selection: 37%|█████████████████████▍ | 10/27 [00:03<00:05, 2.95it/s]
1 Order: (1, 0, 0), AICc: 3467.10, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93 (1, 0, 1) 2
Berlin model selection: 41%|███████████████████████▋ | 11/27 [00:03<00:06, 2.42it/s]
1 Order: (1, 0, 1), AICc: 3445.58, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93 (1, 0, 2) 2
Berlin model selection: 44%|█████████████████████████▊ | 12/27 [00:04<00:06, 2.18it/s]
1 Order: (1, 0, 2), AICc: 3408.12, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93 (1, 1, 0) 2 1 Order: (1, 1, 0), AICc: 3451.88, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93
Berlin model selection: 48%|███████████████████████████▉ | 13/27 [00:04<00:05, 2.67it/s]
(1, 1, 1) 2
Berlin model selection: 52%|██████████████████████████████ | 14/27 [00:04<00:05, 2.54it/s]
1 Order: (1, 1, 1), AICc: 3386.02, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 3386.02 (1, 1, 2) 2
Berlin model selection: 56%|████████████████████████████████▏ | 15/27 [00:05<00:06, 1.93it/s]
1 Order: (1, 1, 2), AICc: 3385.17, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17 (1, 2, 0) 2 1
Berlin model selection: 59%|██████████████████████████████████▎ | 16/27 [00:05<00:04, 2.36it/s]
Order: (1, 2, 0), AICc: 3809.70, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17 (1, 2, 1) 2
Berlin model selection: 63%|████████████████████████████████████▌ | 17/27 [00:06<00:04, 2.28it/s]
1 Order: (1, 2, 1), AICc: 3450.29, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17 (1, 2, 2) 2
Berlin model selection: 67%|██████████████████████████████████████▋ | 18/27 [00:06<00:04, 2.19it/s]
1 Order: (1, 2, 2), AICc: 3454.00, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17 (2, 0, 0) 2
Berlin model selection: 70%|████████████████████████████████████████▊ | 19/27 [00:07<00:03, 2.17it/s]
1 Order: (2, 0, 0), AICc: 3452.01, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17 (2, 0, 1) 2
Berlin model selection: 74%|██████████████████████████████████████████▉ | 20/27 [00:07<00:03, 2.08it/s]
1 Order: (2, 0, 1), AICc: 3403.47, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17 (2, 0, 2) 2
Berlin model selection: 78%|█████████████████████████████████████████████ | 21/27 [00:08<00:03, 1.88it/s]
1 Order: (2, 0, 2), AICc: 3411.14, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17 (2, 1, 0) 2
Berlin model selection: 81%|███████████████████████████████████████████████▎ | 22/27 [00:08<00:02, 2.29it/s]
1 Order: (2, 1, 0), AICc: 3420.92, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17 (2, 1, 1) 2
Berlin model selection: 85%|█████████████████████████████████████████████████▍ | 23/27 [00:09<00:02, 1.96it/s]
1 Order: (2, 1, 1), AICc: 3388.06, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17 (2, 1, 2) 2
Berlin model selection: 89%|███████████████████████████████████████████████████▌ | 24/27 [00:09<00:01, 1.83it/s]
1 Order: (2, 1, 2), AICc: 3382.95, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3382.95 (2, 2, 0) 2
Berlin model selection: 93%|█████████████████████████████████████████████████████▋ | 25/27 [00:10<00:00, 2.14it/s]
1 Order: (2, 2, 0), AICc: 3686.34, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3382.95 (2, 2, 1) 2
Berlin model selection: 96%|███████████████████████████████████████████████████████▊ | 26/27 [00:10<00:00, 1.97it/s]
1 Order: (2, 2, 1), AICc: 3425.25, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3382.95 (2, 2, 2) 2
Berlin model selection: 100%|██████████████████████████████████████████████████████████| 27/27 [00:11<00:00, 2.34it/s]
1 Order: (2, 2, 2), AICc: 3428.10, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3382.95
Analysed test_samples from: 0%| | 0/73 [00:00<?, ?it/s]
1
Analysed test_samples from: 1%|▊ | 1/73 [00:00<00:43, 1.66it/s]
65.62735425396713 1
Analysed test_samples from: 3%|█▌ | 2/73 [00:01<00:44, 1.60it/s]
66.04596746761403 1
Analysed test_samples from: 4%|██▎ | 3/73 [00:01<00:43, 1.62it/s]
65.02259031015114 1
Analysed test_samples from: 5%|███ | 4/73 [00:02<00:43, 1.60it/s]
65.33075436543933 1
Analysed test_samples from: 7%|███▊ | 5/73 [00:03<00:42, 1.61it/s]
64.47700727681377 1
Analysed test_samples from: 8%|████▌ | 6/73 [00:03<00:41, 1.62it/s]
65.82138840866403 1
Analysed test_samples from: 10%|█████▎ | 7/73 [00:04<00:41, 1.61it/s]
66.46641576242206 1
Analysed test_samples from: 11%|██████ | 8/73 [00:05<00:41, 1.56it/s]
67.96672518526083 1
Analysed test_samples from: 12%|██████▊ | 9/73 [00:05<00:40, 1.57it/s]
69.88989595777457 1
Analysed test_samples from: 14%|███████▍ | 10/73 [00:06<00:41, 1.53it/s]
68.3472613486198 1
Analysed test_samples from: 15%|████████▏ | 11/73 [00:07<00:40, 1.53it/s]
67.7661932727435 1
Analysed test_samples from: 16%|████████▉ | 12/73 [00:07<00:39, 1.54it/s]
64.7563071452109 1
Analysed test_samples from: 18%|█████████▌ | 13/73 [00:08<00:39, 1.51it/s]
62.94145806156641 1
Analysed test_samples from: 19%|██████████▎ | 14/73 [00:08<00:38, 1.52it/s]
65.66149156443565 1
Analysed test_samples from: 21%|███████████ | 15/73 [00:09<00:37, 1.55it/s]
68.77876169382836 1
Analysed test_samples from: 22%|███████████▊ | 16/73 [00:10<00:36, 1.57it/s]
67.273077266864 1
Analysed test_samples from: 23%|████████████▌ | 17/73 [00:10<00:34, 1.60it/s]
65.79678817674902 1
Analysed test_samples from: 25%|█████████████▎ | 18/73 [00:11<00:34, 1.58it/s]
66.60840385878116 1
Analysed test_samples from: 26%|██████████████ | 19/73 [00:12<00:33, 1.60it/s]
70.09252839135985 1
Analysed test_samples from: 27%|██████████████▊ | 20/73 [00:12<00:33, 1.57it/s]
68.6417865579439 1
Analysed test_samples from: 29%|███████████████▌ | 21/73 [00:13<00:33, 1.55it/s]
66.0293951618142 1
Analysed test_samples from: 30%|████████████████▎ | 22/73 [00:14<00:33, 1.54it/s]
66.12845971629608 1
Analysed test_samples from: 32%|█████████████████ | 23/73 [00:14<00:33, 1.51it/s]
67.04397706568977 1
Analysed test_samples from: 33%|█████████████████▊ | 24/73 [00:15<00:32, 1.52it/s]
68.88766979844186 1
Analysed test_samples from: 34%|██████████████████▍ | 25/73 [00:16<00:31, 1.55it/s]
65.48812996777579 1
Analysed test_samples from: 36%|███████████████████▏ | 26/73 [00:16<00:30, 1.53it/s]
59.38658488836716 1
Analysed test_samples from: 37%|███████████████████▉ | 27/73 [00:17<00:29, 1.56it/s]
61.424966951148505 1
Analysed test_samples from: 38%|████████████████████▋ | 28/73 [00:17<00:28, 1.56it/s]
59.049593046501954 1
Analysed test_samples from: 40%|█████████████████████▍ | 29/73 [00:18<00:28, 1.56it/s]
57.63779152933907 1
Analysed test_samples from: 41%|██████████████████████▏ | 30/73 [00:19<00:26, 1.60it/s]
56.29560924655715 1
Analysed test_samples from: 42%|██████████████████████▉ | 31/73 [00:19<00:26, 1.58it/s]
60.55365144137927 1
Analysed test_samples from: 44%|███████████████████████▋ | 32/73 [00:20<00:26, 1.57it/s]
61.337528607639186 1
Analysed test_samples from: 45%|████████████████████████▍ | 33/73 [00:21<00:25, 1.58it/s]
60.80728420064129 1
Analysed test_samples from: 47%|█████████████████████████▏ | 34/73 [00:21<00:24, 1.60it/s]
62.42423941010057 1
Analysed test_samples from: 48%|█████████████████████████▉ | 35/73 [00:22<00:24, 1.57it/s]
62.901868751111536 1
Analysed test_samples from: 49%|██████████████████████████▋ | 36/73 [00:23<00:23, 1.56it/s]
64.35923859683317 1
Analysed test_samples from: 51%|███████████████████████████▎ | 37/73 [00:23<00:23, 1.52it/s]
62.72782336724349 1
Analysed test_samples from: 52%|████████████████████████████ | 38/73 [00:24<00:22, 1.52it/s]
62.43468893508983 1
Analysed test_samples from: 53%|████████████████████████████▊ | 39/73 [00:24<00:22, 1.54it/s]
63.58430169042256 1
Analysed test_samples from: 55%|█████████████████████████████▌ | 40/73 [00:25<00:21, 1.54it/s]
63.99906412550196 1
Analysed test_samples from: 56%|██████████████████████████████▎ | 41/73 [00:26<00:20, 1.56it/s]
62.80938804851184 1
Analysed test_samples from: 58%|███████████████████████████████ | 42/73 [00:26<00:20, 1.54it/s]
61.44899481253 1
Analysed test_samples from: 59%|███████████████████████████████▊ | 43/73 [00:27<00:19, 1.54it/s]
62.12163191455666 1
Analysed test_samples from: 60%|████████████████████████████████▌ | 44/73 [00:28<00:18, 1.53it/s]
63.19348301085421 1
Analysed test_samples from: 62%|█████████████████████████████████▎ | 45/73 [00:28<00:18, 1.48it/s]
60.07348982268695 1
Analysed test_samples from: 63%|██████████████████████████████████ | 46/73 [00:29<00:18, 1.48it/s]
57.763549191315846 1
Analysed test_samples from: 64%|██████████████████████████████████▊ | 47/73 [00:30<00:17, 1.48it/s]
56.96433315579284 1
Analysed test_samples from: 66%|███████████████████████████████████▌ | 48/73 [00:31<00:17, 1.45it/s]
59.54990980205554 1
Analysed test_samples from: 67%|████████████████████████████████████▏ | 49/73 [00:31<00:16, 1.49it/s]
63.75640113170583 1
Analysed test_samples from: 68%|████████████████████████████████████▉ | 50/73 [00:32<00:15, 1.50it/s]
65.02923975434707 1
Analysed test_samples from: 70%|█████████████████████████████████████▋ | 51/73 [00:33<00:14, 1.49it/s]
63.97926027850823 1
Analysed test_samples from: 71%|██████████████████████████████████████▍ | 52/73 [00:33<00:13, 1.50it/s]
64.03154517202826 1
Analysed test_samples from: 73%|███████████████████████████████████████▏ | 53/73 [00:34<00:13, 1.48it/s]
62.63677859792348 1
Analysed test_samples from: 74%|███████████████████████████████████████▉ | 54/73 [00:35<00:12, 1.47it/s]
65.14138419155714 1
Analysed test_samples from: 75%|████████████████████████████████████████▋ | 55/73 [00:35<00:12, 1.47it/s]
64.74996202272379 1
Analysed test_samples from: 77%|█████████████████████████████████████████▍ | 56/73 [00:36<00:11, 1.49it/s]
64.58331309753723 1
Analysed test_samples from: 78%|██████████████████████████████████████████▏ | 57/73 [00:37<00:10, 1.50it/s]
65.20096144367982 1
Analysed test_samples from: 79%|██████████████████████████████████████████▉ | 58/73 [00:37<00:10, 1.47it/s]
63.4112012029537 1
Analysed test_samples from: 81%|███████████████████████████████████████████▋ | 59/73 [00:38<00:09, 1.49it/s]
62.65985004200465 1
Analysed test_samples from: 82%|████████████████████████████████████████████▍ | 60/73 [00:39<00:08, 1.50it/s]
59.24749887862634 1
Analysed test_samples from: 84%|█████████████████████████████████████████████ | 61/73 [00:39<00:07, 1.50it/s]
60.37201590819814 1
Analysed test_samples from: 85%|█████████████████████████████████████████████▊ | 62/73 [00:40<00:07, 1.48it/s]
61.831829502808965 1
Analysed test_samples from: 86%|██████████████████████████████████████████████▌ | 63/73 [00:41<00:06, 1.47it/s]
61.0264464323552 1
Analysed test_samples from: 88%|███████████████████████████████████████████████▎ | 64/73 [00:41<00:06, 1.47it/s]
63.80868571080629 1
Analysed test_samples from: 89%|████████████████████████████████████████████████ | 65/73 [00:42<00:05, 1.48it/s]
61.42898736417973 1
Analysed test_samples from: 90%|████████████████████████████████████████████████▊ | 66/73 [00:43<00:04, 1.47it/s]
59.73733562547491 1
Analysed test_samples from: 92%|█████████████████████████████████████████████████▌ | 67/73 [00:43<00:04, 1.49it/s]
56.39526596975725 1
Analysed test_samples from: 93%|██████████████████████████████████████████████████▎ | 68/73 [00:44<00:03, 1.49it/s]
61.442864739674874 1
Analysed test_samples from: 95%|███████████████████████████████████████████████████ | 69/73 [00:45<00:02, 1.47it/s]
59.17066250521117 1
Analysed test_samples from: 96%|███████████████████████████████████████████████████▊ | 70/73 [00:45<00:02, 1.46it/s]
62.03589437080747 1
Analysed test_samples from: 97%|████████████████████████████████████████████████████▌ | 71/73 [00:46<00:01, 1.46it/s]
58.25804156264113 1
Analysed test_samples from: 99%|█████████████████████████████████████████████████████▎| 72/73 [00:47<00:00, 1.43it/s]
58.189705758911245 1
Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:48<00:00, 1.52it/s]
56.115361560734065 False False
Bruksela model selection: 0%| | 0/27 [00:00<?, ?it/s]
(0, 0, 0) 2
Bruksela model selection: 4%|██ | 1/27 [00:00<00:06, 4.13it/s]
1 Order: (0, 0, 0), AICc: 3881.25, ADF p-value: 0.1478, Best_Order: (0, 0, 0), Best_aicc: inf (0, 0, 1) 2
Bruksela model selection: 7%|████▏ | 2/27 [00:00<00:05, 4.32it/s]
1 Order: (0, 0, 1), AICc: 3552.08, ADF p-value: 0.1694, Best_Order: (0, 0, 0), Best_aicc: inf (0, 0, 2) 2
Bruksela model selection: 11%|██████▎ | 3/27 [00:00<00:06, 3.83it/s]
1 Order: (0, 0, 2), AICc: 3445.11, ADF p-value: 0.2198, Best_Order: (0, 0, 0), Best_aicc: inf (0, 1, 0) 2 1 Order: (0, 1, 0), AICc: 3283.37, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3283.37
Bruksela model selection: 19%|██████████▌ | 5/27 [00:01<00:04, 5.00it/s]
(0, 1, 1) 2 1 Order: (0, 1, 1), AICc: 3266.65, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3266.65 (0, 1, 2) 2
Bruksela model selection: 22%|████████████▋ | 6/27 [00:01<00:04, 4.28it/s]
1 Order: (0, 1, 2), AICc: 3204.15, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15 (0, 2, 0) 2
Bruksela model selection: 26%|██████████████▊ | 7/27 [00:01<00:04, 4.37it/s]
1 Order: (0, 2, 0), AICc: 3784.23, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15 (0, 2, 1) 2
Bruksela model selection: 30%|████████████████▉ | 8/27 [00:01<00:04, 3.89it/s]
1 Order: (0, 2, 1), AICc: 3282.20, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15 (0, 2, 2) 2
Bruksela model selection: 33%|███████████████████ | 9/27 [00:02<00:06, 2.81it/s]
1 Order: (0, 2, 2), AICc: 3266.10, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15 (1, 0, 0) 2
Bruksela model selection: 37%|████████████████████▋ | 10/27 [00:02<00:05, 2.87it/s]
1 Order: (1, 0, 0), AICc: 3286.86, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15 (1, 0, 1) 2
Bruksela model selection: 41%|██████████████████████▊ | 11/27 [00:03<00:05, 2.92it/s]
1 Order: (1, 0, 1), AICc: 3270.14, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15 (1, 0, 2) 2
Bruksela model selection: 48%|██████████████████████████▉ | 13/27 [00:03<00:04, 3.01it/s]
1 Order: (1, 0, 2), AICc: 3210.17, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15 (1, 1, 0) 2 1 Order: (1, 1, 0), AICc: 3278.71, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15 (1, 1, 1) 2
Bruksela model selection: 52%|█████████████████████████████ | 14/27 [00:04<00:04, 3.07it/s]
1 Order: (1, 1, 1), AICc: 3202.23, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 3202.23 (1, 1, 2) 2
Bruksela model selection: 56%|███████████████████████████████ | 15/27 [00:04<00:03, 3.16it/s]
1 Order: (1, 1, 2), AICc: 3191.82, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82 (1, 2, 0) 2
Bruksela model selection: 59%|█████████████████████████████████▏ | 16/27 [00:04<00:03, 3.42it/s]
1 Order: (1, 2, 0), AICc: 3651.72, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82 (1, 2, 1) 2
Bruksela model selection: 63%|███████████████████████████████████▎ | 17/27 [00:05<00:03, 3.07it/s]
1 Order: (1, 2, 1), AICc: 3278.25, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82 (1, 2, 2) 2
Bruksela model selection: 67%|█████████████████████████████████████▎ | 18/27 [00:05<00:03, 2.66it/s]
1 Order: (1, 2, 2), AICc: 3281.66, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82 (2, 0, 0) 2
Bruksela model selection: 70%|███████████████████████████████████████▍ | 19/27 [00:05<00:02, 2.69it/s]
1 Order: (2, 0, 0), AICc: 3277.52, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82 (2, 0, 1) 2
Bruksela model selection: 74%|█████████████████████████████████████████▍ | 20/27 [00:06<00:03, 2.16it/s]
1 Order: (2, 0, 1), AICc: 3211.44, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82 (2, 0, 2) 2
Bruksela model selection: 78%|███████████████████████████████████████████▌ | 21/27 [00:07<00:03, 1.85it/s]
1 Order: (2, 0, 2), AICc: 3198.62, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82 (2, 1, 0) 2
Bruksela model selection: 81%|█████████████████████████████████████████████▋ | 22/27 [00:07<00:02, 2.23it/s]
1 Order: (2, 1, 0), AICc: 3240.33, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82 (2, 1, 1) 2
Bruksela model selection: 85%|███████████████████████████████████████████████▋ | 23/27 [00:07<00:01, 2.62it/s]
1 Order: (2, 1, 1), AICc: 3196.82, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82 (2, 1, 2) 2
Bruksela model selection: 89%|█████████████████████████████████████████████████▊ | 24/27 [00:08<00:01, 2.33it/s]
1 Order: (2, 1, 2), AICc: 3193.86, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82 (2, 2, 0) 2
Bruksela model selection: 93%|███████████████████████████████████████████████████▊ | 25/27 [00:08<00:00, 2.65it/s]
1 Order: (2, 2, 0), AICc: 3549.39, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82 (2, 2, 1) 2
Bruksela model selection: 96%|█████████████████████████████████████████████████████▉ | 26/27 [00:09<00:00, 2.38it/s]
1 Order: (2, 2, 1), AICc: 3244.95, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82 (2, 2, 2) 2
Bruksela model selection: 100%|████████████████████████████████████████████████████████| 27/27 [00:09<00:00, 2.71it/s]
1 Order: (2, 2, 2), AICc: 3206.50, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
Analysed test_samples from: 0%| | 0/73 [00:00<?, ?it/s]
1
Analysed test_samples from: 1%|▊ | 1/73 [00:00<00:20, 3.59it/s]
60.81001150769126 1
Analysed test_samples from: 3%|█▌ | 2/73 [00:00<00:18, 3.89it/s]
57.021253422707716 1
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57.03606452556754 1
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55.01907603090048 1
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55.948979111590106 1
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54.658572212946716 1
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54.103500021108545 1
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55.95261558620846 1
Analysed test_samples from: 12%|██████▊ | 9/73 [00:02<00:15, 4.07it/s]
56.17397681510489 1
Analysed test_samples from: 14%|███████▍ | 10/73 [00:02<00:15, 4.10it/s]
55.68982270831185 1
Analysed test_samples from: 15%|████████▏ | 11/73 [00:02<00:15, 4.10it/s]
56.66548505232444 1
Analysed test_samples from: 16%|████████▉ | 12/73 [00:02<00:14, 4.10it/s]
55.59166201959909 1
Analysed test_samples from: 18%|█████████▌ | 13/73 [00:03<00:14, 4.11it/s]
56.28768461615111 1
Analysed test_samples from: 19%|██████████▎ | 14/73 [00:03<00:14, 4.05it/s]
53.5841019455057 1
Analysed test_samples from: 21%|███████████ | 15/73 [00:03<00:14, 4.10it/s]
51.219879735610576 1
Analysed test_samples from: 22%|███████████▊ | 16/73 [00:03<00:13, 4.14it/s]
53.3326919553966 1
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54.36838768775253 1
Analysed test_samples from: 25%|█████████████▎ | 18/73 [00:04<00:13, 4.20it/s]
54.29955535061973 1
Analysed test_samples from: 26%|██████████████ | 19/73 [00:04<00:13, 4.12it/s]
52.843502151506506 1
Analysed test_samples from: 27%|██████████████▊ | 20/73 [00:04<00:12, 4.16it/s]
52.65302903101675 1
Analysed test_samples from: 29%|███████████████▌ | 21/73 [00:05<00:12, 4.11it/s]
52.651271787138796 1
Analysed test_samples from: 30%|████████████████▎ | 22/73 [00:05<00:12, 4.09it/s]
51.99497412458004 1
Analysed test_samples from: 32%|█████████████████ | 23/73 [00:05<00:12, 4.13it/s]
51.44014402251629 1
Analysed test_samples from: 33%|█████████████████▊ | 24/73 [00:05<00:12, 4.06it/s]
50.41515536370429 1
Analysed test_samples from: 34%|██████████████████▍ | 25/73 [00:06<00:11, 4.02it/s]
55.54610945878348 1
Analysed test_samples from: 36%|███████████████████▏ | 26/73 [00:06<00:11, 4.06it/s]
54.728938421270556 1
Analysed test_samples from: 37%|███████████████████▉ | 27/73 [00:06<00:11, 4.08it/s]
53.35420269007324 1
Analysed test_samples from: 38%|████████████████████▋ | 28/73 [00:06<00:11, 4.05it/s]
51.8395168163064 1
Analysed test_samples from: 40%|█████████████████████▍ | 29/73 [00:07<00:10, 4.01it/s]
50.643930287904624 1
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53.23902494095577 1
Analysed test_samples from: 42%|██████████████████████▉ | 31/73 [00:07<00:10, 4.05it/s]
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Analysed test_samples from: 44%|███████████████████████▋ | 32/73 [00:07<00:10, 4.04it/s]
54.1165298561657 1
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52.20990181817291 1
Analysed test_samples from: 47%|█████████████████████████▏ | 34/73 [00:08<00:10, 3.82it/s]
47.24249095731429 1
Analysed test_samples from: 48%|█████████████████████████▉ | 35/73 [00:08<00:09, 3.87it/s]
54.3728755445471 1
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49.98605375393234 1
Analysed test_samples from: 51%|███████████████████████████▎ | 37/73 [00:09<00:09, 3.89it/s]
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43.911201247764026 1
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46.674255019387836 1
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41.28191729291823 1
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40.88921155203411 1
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41.59568943454349 1
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44.64359163464049 1
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45.801298075229155 1
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47.40695262177471 1
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44.941753193899046 1
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50.81491486660384 1
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52.34091738459968 1
Analysed test_samples from: 71%|██████████████████████████████████████▍ | 52/73 [00:13<00:05, 3.87it/s]
52.59518974727124 1
Analysed test_samples from: 73%|███████████████████████████████████████▏ | 53/73 [00:13<00:05, 3.88it/s]
53.01581413411577 1
Analysed test_samples from: 74%|███████████████████████████████████████▉ | 54/73 [00:13<00:04, 3.98it/s]
54.09160202261134 1
Analysed test_samples from: 75%|████████████████████████████████████████▋ | 55/73 [00:13<00:04, 4.00it/s]
53.15323396300221 1
Analysed test_samples from: 77%|█████████████████████████████████████████▍ | 56/73 [00:14<00:04, 3.98it/s]
49.5853057650359 1
Analysed test_samples from: 78%|██████████████████████████████████████████▏ | 57/73 [00:14<00:04, 3.92it/s]
49.94251474914698 1
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52.138533397277186 1
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49.39146973011266 1
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48.60537643082395 1
Analysed test_samples from: 84%|█████████████████████████████████████████████ | 61/73 [00:15<00:03, 3.89it/s]
51.01229964929651 1
Analysed test_samples from: 85%|█████████████████████████████████████████████▊ | 62/73 [00:15<00:02, 3.93it/s]
51.163694519011855 1
Analysed test_samples from: 86%|██████████████████████████████████████████████▌ | 63/73 [00:15<00:02, 3.94it/s]
54.089626509317085 1
Analysed test_samples from: 88%|███████████████████████████████████████████████▎ | 64/73 [00:16<00:02, 3.95it/s]
52.59671426401515 1
Analysed test_samples from: 89%|████████████████████████████████████████████████ | 65/73 [00:16<00:02, 3.86it/s]
52.98380259141444 1
Analysed test_samples from: 90%|████████████████████████████████████████████████▊ | 66/73 [00:16<00:01, 3.87it/s]
54.84801215166554 1
Analysed test_samples from: 92%|█████████████████████████████████████████████████▌ | 67/73 [00:16<00:01, 3.89it/s]
54.20493709787149 1
Analysed test_samples from: 93%|██████████████████████████████████████████████████▎ | 68/73 [00:17<00:01, 3.95it/s]
51.42861130220196 1
Analysed test_samples from: 95%|███████████████████████████████████████████████████ | 69/73 [00:17<00:01, 3.93it/s]
51.36420471046843 1
Analysed test_samples from: 96%|███████████████████████████████████████████████████▊ | 70/73 [00:17<00:00, 3.94it/s]
53.30673323384386 1
Analysed test_samples from: 97%|████████████████████████████████████████████████████▌ | 71/73 [00:17<00:00, 3.99it/s]
53.54117629443261 1
Analysed test_samples from: 99%|█████████████████████████████████████████████████████▎| 72/73 [00:18<00:00, 3.92it/s]
53.20547839426329 1
Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:18<00:00, 3.97it/s]
51.2638528773447 False False
Budapeszt model selection: 0%| | 0/27 [00:00<?, ?it/s]
(0, 0, 0) 2
Budapeszt model selection: 4%|██ | 1/27 [00:00<00:08, 2.94it/s]
1 Order: (0, 0, 0), AICc: 4433.95, ADF p-value: 0.0018, Best_Order: (0, 0, 0), Best_aicc: 4433.95 (0, 0, 1) 2
Budapeszt model selection: 7%|████▏ | 2/27 [00:00<00:09, 2.69it/s]
1 Order: (0, 0, 1), AICc: 4117.49, ADF p-value: 0.0741, Best_Order: (0, 0, 0), Best_aicc: 4433.95 (0, 0, 2) 2
Budapeszt model selection: 15%|████████▎ | 4/27 [00:01<00:07, 3.02it/s]
1 Order: (0, 0, 2), AICc: 3948.06, ADF p-value: 0.1780, Best_Order: (0, 0, 0), Best_aicc: 4433.95 (0, 1, 0) 2 1 Order: (0, 1, 0), AICc: 3490.36, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3490.36 (0, 1, 1) 2
Budapeszt model selection: 19%|██████████▎ | 5/27 [00:01<00:05, 3.74it/s]
1 Order: (0, 1, 1), AICc: 3478.47, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3478.47 (0, 1, 2) 2
Budapeszt model selection: 22%|████████████▍ | 6/27 [00:01<00:05, 3.90it/s]
1 Order: (0, 1, 2), AICc: 3425.83, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83 (0, 2, 0) 2
Budapeszt model selection: 26%|██████████████▌ | 7/27 [00:02<00:04, 4.23it/s]
1 Order: (0, 2, 0), AICc: 3960.10, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83 (0, 2, 1) 2
Budapeszt model selection: 30%|████████████████▌ | 8/27 [00:02<00:05, 3.59it/s]
1 Order: (0, 2, 1), AICc: 3485.14, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83 (0, 2, 2) 2
Budapeszt model selection: 33%|██████████████████▋ | 9/27 [00:02<00:06, 2.80it/s]
1 Order: (0, 2, 2), AICc: 3476.87, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83 (1, 0, 0) 2
Budapeszt model selection: 37%|████████████████████▎ | 10/27 [00:03<00:05, 2.97it/s]
1 Order: (1, 0, 0), AICc: 3481.83, ADF p-value: 0.0016, Best_Order: (0, 1, 2), Best_aicc: 3425.83 (1, 0, 1) 2
Budapeszt model selection: 41%|██████████████████████▍ | 11/27 [00:03<00:06, 2.61it/s]
1 Order: (1, 0, 1), AICc: 3475.36, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83 (1, 0, 2) 2
Budapeszt model selection: 48%|██████████████████████████▍ | 13/27 [00:04<00:05, 2.74it/s]
1 Order: (1, 0, 2), AICc: 3431.69, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83 (1, 1, 0) 2 1 Order: (1, 1, 0), AICc: 3488.77, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83 (1, 1, 1) 2
Budapeszt model selection: 52%|████████████████████████████▌ | 14/27 [00:04<00:04, 2.97it/s]
1 Order: (1, 1, 1), AICc: 3426.20, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83 (1, 1, 2) 2
Budapeszt model selection: 56%|██████████████████████████████▌ | 15/27 [00:05<00:03, 3.04it/s]
1 Order: (1, 1, 2), AICc: 3411.65, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65 (1, 2, 0) 2 1
Budapeszt model selection: 59%|████████████████████████████████▌ | 16/27 [00:05<00:03, 3.49it/s]
Order: (1, 2, 0), AICc: 3839.78, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65 (1, 2, 1) 2
Budapeszt model selection: 63%|██████████████████████████████████▋ | 17/27 [00:05<00:03, 3.33it/s]
1 Order: (1, 2, 1), AICc: 3484.68, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65 (1, 2, 2) 2
Budapeszt model selection: 67%|████████████████████████████████████▋ | 18/27 [00:06<00:03, 2.61it/s]
1 Order: (1, 2, 2), AICc: 3483.99, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65 (2, 0, 0) 2
Budapeszt model selection: 70%|██████████████████████████████████████▋ | 19/27 [00:06<00:03, 2.48it/s]
1 Order: (2, 0, 0), AICc: 3476.72, ADF p-value: 0.0008, Best_Order: (1, 1, 2), Best_aicc: 3411.65 (2, 0, 1) 2
Budapeszt model selection: 74%|████████████████████████████████████████▋ | 20/27 [00:07<00:03, 2.26it/s]
1 Order: (2, 0, 1), AICc: 3496.21, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65 (2, 0, 2) 2
Budapeszt model selection: 78%|██████████████████████████████████████████▊ | 21/27 [00:07<00:02, 2.02it/s]
1 Order: (2, 0, 2), AICc: 3522.94, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65 (2, 1, 0) 2 1
Budapeszt model selection: 81%|████████████████████████████████████████████▊ | 22/27 [00:07<00:02, 2.48it/s]
Order: (2, 1, 0), AICc: 3455.50, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65 (2, 1, 1) 2
Budapeszt model selection: 85%|██████████████████████████████████████████████▊ | 23/27 [00:08<00:01, 2.73it/s]
1 Order: (2, 1, 1), AICc: 3419.94, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65 (2, 1, 2) 2
Budapeszt model selection: 89%|████████████████████████████████████████████████▉ | 24/27 [00:08<00:01, 2.56it/s]
1 Order: (2, 1, 2), AICc: 3413.64, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65 (2, 2, 0) 2
Budapeszt model selection: 93%|██████████████████████████████████████████████████▉ | 25/27 [00:08<00:00, 2.93it/s]
1 Order: (2, 2, 0), AICc: 3741.82, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65 (2, 2, 1) 2
Budapeszt model selection: 96%|████████████████████████████████████████████████████▉ | 26/27 [00:09<00:00, 2.49it/s]
1 Order: (2, 2, 1), AICc: 3459.78, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65 (2, 2, 2) 2
Budapeszt model selection: 100%|███████████████████████████████████████████████████████| 27/27 [00:10<00:00, 2.65it/s]
1 Order: (2, 2, 2), AICc: 3417.81, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65
Analysed test_samples from: 0%| | 0/73 [00:00<?, ?it/s]
1
Analysed test_samples from: 1%|▊ | 1/73 [00:00<00:21, 3.37it/s]
55.171419468172544 1
Analysed test_samples from: 3%|█▌ | 2/73 [00:00<00:19, 3.60it/s]
57.383272212710935 1
Analysed test_samples from: 4%|██▎ | 3/73 [00:00<00:20, 3.42it/s]
56.18133361044262 1
Analysed test_samples from: 5%|███ | 4/73 [00:01<00:20, 3.33it/s]
54.93486522007264 1
Analysed test_samples from: 7%|███▊ | 5/73 [00:01<00:20, 3.25it/s]
56.38577807648289 1
Analysed test_samples from: 8%|████▌ | 6/73 [00:01<00:19, 3.36it/s]
55.94445624613602 1
Analysed test_samples from: 10%|█████▎ | 7/73 [00:02<00:19, 3.37it/s]
55.64958667500238 1
Analysed test_samples from: 11%|██████ | 8/73 [00:02<00:19, 3.35it/s]
55.96501014033744 1
Analysed test_samples from: 12%|██████▊ | 9/73 [00:02<00:19, 3.36it/s]
54.621752813112806 1
Analysed test_samples from: 14%|███████▍ | 10/73 [00:02<00:18, 3.42it/s]
54.871327463503576 1
Analysed test_samples from: 15%|████████▏ | 11/73 [00:03<00:18, 3.34it/s]
54.95081635825984 1
Analysed test_samples from: 16%|████████▉ | 12/73 [00:03<00:18, 3.26it/s]
54.22023272799812 1
Analysed test_samples from: 18%|█████████▌ | 13/73 [00:03<00:18, 3.22it/s]
52.94257297783219 1
Analysed test_samples from: 19%|██████████▎ | 14/73 [00:04<00:18, 3.18it/s]
53.21881825946278 1
Analysed test_samples from: 21%|███████████ | 15/73 [00:04<00:18, 3.12it/s]
54.04744189070413 1
Analysed test_samples from: 22%|███████████▊ | 16/73 [00:04<00:17, 3.17it/s]
51.515703078891555 1
Analysed test_samples from: 23%|████████████▌ | 17/73 [00:05<00:17, 3.19it/s]
53.77117696113525 1
Analysed test_samples from: 25%|█████████████▎ | 18/73 [00:05<00:16, 3.24it/s]
52.226448856029805 1
Analysed test_samples from: 26%|██████████████ | 19/73 [00:05<00:16, 3.30it/s]
53.731907197977215 1
Analysed test_samples from: 27%|██████████████▊ | 20/73 [00:06<00:16, 3.27it/s]
50.84590298418686 1
Analysed test_samples from: 29%|███████████████▌ | 21/73 [00:06<00:15, 3.34it/s]
49.8256910959653 1
Analysed test_samples from: 30%|████████████████▎ | 22/73 [00:06<00:14, 3.42it/s]
51.364400519558956 1
Analysed test_samples from: 32%|█████████████████ | 23/73 [00:06<00:14, 3.41it/s]
51.50182100143126 1
Analysed test_samples from: 33%|█████████████████▊ | 24/73 [00:07<00:13, 3.52it/s]
48.32640481501648 1
Analysed test_samples from: 34%|██████████████████▍ | 25/73 [00:07<00:13, 3.50it/s]
50.18076781317771 1
Analysed test_samples from: 36%|███████████████████▏ | 26/73 [00:07<00:13, 3.54it/s]
53.63267322187731 1
Analysed test_samples from: 37%|███████████████████▉ | 27/73 [00:08<00:12, 3.54it/s]
51.94496127191063 1
Analysed test_samples from: 38%|████████████████████▋ | 28/73 [00:08<00:13, 3.42it/s]
50.89089628648329 1
Analysed test_samples from: 40%|█████████████████████▍ | 29/73 [00:08<00:12, 3.40it/s]
50.757657980426096 1
Analysed test_samples from: 41%|██████████████████████▏ | 30/73 [00:08<00:13, 3.30it/s]
47.41879632730064 1
Analysed test_samples from: 42%|██████████████████████▉ | 31/73 [00:09<00:12, 3.37it/s]
48.28683013056282 1
Analysed test_samples from: 44%|███████████████████████▋ | 32/73 [00:09<00:11, 3.42it/s]
51.167093237752354 1
Analysed test_samples from: 45%|████████████████████████▍ | 33/73 [00:09<00:11, 3.39it/s]
50.96982926153988 1
Analysed test_samples from: 47%|█████████████████████████▏ | 34/73 [00:10<00:11, 3.38it/s]
49.15486224380918 1
Analysed test_samples from: 48%|█████████████████████████▉ | 35/73 [00:10<00:11, 3.34it/s]
46.98492260759619 1
Analysed test_samples from: 49%|██████████████████████████▋ | 36/73 [00:10<00:11, 3.35it/s]
48.36585514260303 1
Analysed test_samples from: 51%|███████████████████████████▎ | 37/73 [00:11<00:10, 3.46it/s]
45.60395882359695 1
Analysed test_samples from: 52%|████████████████████████████ | 38/73 [00:11<00:10, 3.46it/s]
44.538691147708676 1
Analysed test_samples from: 53%|████████████████████████████▊ | 39/73 [00:11<00:09, 3.42it/s]
46.90599512644454 1
Analysed test_samples from: 55%|█████████████████████████████▌ | 40/73 [00:11<00:09, 3.51it/s]
49.410785007507535 1
Analysed test_samples from: 56%|██████████████████████████████▎ | 41/73 [00:12<00:09, 3.48it/s]
44.42027723035327 1
Analysed test_samples from: 58%|███████████████████████████████ | 42/73 [00:12<00:09, 3.39it/s]
46.550920742217514 1
Analysed test_samples from: 59%|███████████████████████████████▊ | 43/73 [00:12<00:08, 3.36it/s]
47.69331962295414 1
Analysed test_samples from: 60%|████████████████████████████████▌ | 44/73 [00:13<00:08, 3.38it/s]
48.34255288449433 1
Analysed test_samples from: 62%|█████████████████████████████████▎ | 45/73 [00:13<00:08, 3.37it/s]
43.2541054149945 1
Analysed test_samples from: 63%|██████████████████████████████████ | 46/73 [00:13<00:08, 3.34it/s]
43.47653422714841 1
Analysed test_samples from: 64%|██████████████████████████████████▊ | 47/73 [00:13<00:07, 3.30it/s]
45.84392486682155 1
Analysed test_samples from: 66%|███████████████████████████████████▌ | 48/73 [00:14<00:07, 3.19it/s]
47.61863037454897 1
Analysed test_samples from: 67%|████████████████████████████████████▏ | 49/73 [00:14<00:07, 3.16it/s]
47.405018805080125 1
Analysed test_samples from: 68%|████████████████████████████████████▉ | 50/73 [00:14<00:07, 3.18it/s]
47.214144929517055 1
Analysed test_samples from: 70%|█████████████████████████████████████▋ | 51/73 [00:15<00:06, 3.16it/s]
46.988207499774305 1
Analysed test_samples from: 71%|██████████████████████████████████████▍ | 52/73 [00:15<00:06, 3.12it/s]
47.32841724806599 1
Analysed test_samples from: 73%|███████████████████████████████████████▏ | 53/73 [00:15<00:06, 3.11it/s]
47.885496681951295 1
Analysed test_samples from: 74%|███████████████████████████████████████▉ | 54/73 [00:16<00:06, 3.13it/s]
49.192847588550016 1
Analysed test_samples from: 75%|████████████████████████████████████████▋ | 55/73 [00:16<00:05, 3.11it/s]
50.14741146051948 1
Analysed test_samples from: 77%|█████████████████████████████████████████▍ | 56/73 [00:16<00:05, 3.15it/s]
50.15355679967736 1
Analysed test_samples from: 78%|██████████████████████████████████████████▏ | 57/73 [00:17<00:05, 3.11it/s]
48.842708565270094 1
Analysed test_samples from: 79%|██████████████████████████████████████████▉ | 58/73 [00:17<00:04, 3.13it/s]
46.79093514008625 1
Analysed test_samples from: 81%|███████████████████████████████████████████▋ | 59/73 [00:17<00:04, 3.09it/s]
47.7773701633514 1
Analysed test_samples from: 82%|████████████████████████████████████████████▍ | 60/73 [00:18<00:04, 3.11it/s]
47.38279620841827 1
Analysed test_samples from: 84%|█████████████████████████████████████████████ | 61/73 [00:18<00:03, 3.09it/s]
46.080705960546894 1
Analysed test_samples from: 85%|█████████████████████████████████████████████▊ | 62/73 [00:18<00:03, 3.09it/s]
47.422219711301 1
Analysed test_samples from: 86%|██████████████████████████████████████████████▌ | 63/73 [00:19<00:03, 3.07it/s]
48.64538851438898 1
Analysed test_samples from: 88%|███████████████████████████████████████████████▎ | 64/73 [00:19<00:02, 3.06it/s]
47.97874130416601 1
Analysed test_samples from: 89%|████████████████████████████████████████████████ | 65/73 [00:19<00:02, 3.04it/s]
47.21415568563646 1
Analysed test_samples from: 90%|████████████████████████████████████████████████▊ | 66/73 [00:20<00:02, 3.09it/s]
49.34904919289276 1
Analysed test_samples from: 92%|█████████████████████████████████████████████████▌ | 67/73 [00:20<00:01, 3.13it/s]
52.07786738464221 1
Analysed test_samples from: 93%|██████████████████████████████████████████████████▎ | 68/73 [00:20<00:01, 3.15it/s]
50.69692358668749 1
Analysed test_samples from: 95%|███████████████████████████████████████████████████ | 69/73 [00:21<00:01, 3.17it/s]
48.72416114322055 1
Analysed test_samples from: 96%|███████████████████████████████████████████████████▊ | 70/73 [00:21<00:00, 3.19it/s]
48.72418550619625 1
Analysed test_samples from: 97%|████████████████████████████████████████████████████▌ | 71/73 [00:21<00:00, 3.23it/s]
48.76365325433997 1
Analysed test_samples from: 99%|█████████████████████████████████████████████████████▎| 72/73 [00:21<00:00, 3.26it/s]
49.0398584623111 1
Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:22<00:00, 3.27it/s]
50.065724442941736 False False
Lisbona model selection: 0%| | 0/27 [00:00<?, ?it/s]
(0, 0, 0) 2
Lisbona model selection: 7%|████▎ | 2/27 [00:00<00:05, 4.78it/s]
1 Order: (0, 0, 0), AICc: 4000.18, ADF p-value: 0.0000, Best_Order: (0, 0, 0), Best_aicc: 4000.18 (0, 0, 1) 2 1 Order: (0, 0, 1), AICc: 3687.00, ADF p-value: 0.0001, Best_Order: (0, 0, 1), Best_aicc: 3687.00 (0, 0, 2) 2
Lisbona model selection: 15%|████████▌ | 4/27 [00:01<00:06, 3.82it/s]
1 Order: (0, 0, 2), AICc: 3541.99, ADF p-value: 0.0263, Best_Order: (0, 0, 2), Best_aicc: 3541.99 (0, 1, 0) 2 1 Order: (0, 1, 0), AICc: 3093.81, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3093.81 (0, 1, 1) 2
Lisbona model selection: 19%|██████████▋ | 5/27 [00:01<00:04, 4.58it/s]
1 Order: (0, 1, 1), AICc: 3090.77, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3090.77 (0, 1, 2) 2 1
Lisbona model selection: 22%|████████████▉ | 6/27 [00:01<00:04, 4.81it/s]
Order: (0, 1, 2), AICc: 3029.69, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69 (0, 2, 0) 2 1 Order: (0, 2, 0), AICc: 3528.96, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69
Lisbona model selection: 26%|███████████████ | 7/27 [00:01<00:03, 5.04it/s]
(0, 2, 1) 2
Lisbona model selection: 30%|█████████████████▏ | 8/27 [00:01<00:04, 3.91it/s]
1 Order: (0, 2, 1), AICc: 3093.24, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69 (0, 2, 2) 2
Lisbona model selection: 33%|███████████████████▎ | 9/27 [00:02<00:05, 3.10it/s]
1 Order: (0, 2, 2), AICc: 3086.83, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69 (1, 0, 0) 2
Lisbona model selection: 37%|█████████████████████ | 10/27 [00:02<00:05, 3.06it/s]
1 Order: (1, 0, 0), AICc: 3078.68, ADF p-value: 0.0112, Best_Order: (0, 1, 2), Best_aicc: 3029.69 (1, 0, 1) 2
Lisbona model selection: 41%|███████████████████████▏ | 11/27 [00:03<00:06, 2.59it/s]
1 Order: (1, 0, 1), AICc: 3090.07, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69 (1, 0, 2) 2
Lisbona model selection: 48%|███████████████████████████▍ | 13/27 [00:04<00:05, 2.73it/s]
1 Order: (1, 0, 2), AICc: 3143.61, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69 (1, 1, 0) 2 1 Order: (1, 1, 0), AICc: 3095.69, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69 (1, 1, 1) 2
Lisbona model selection: 52%|█████████████████████████████▌ | 14/27 [00:04<00:04, 3.01it/s]
1 Order: (1, 1, 1), AICc: 3038.97, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69 (1, 1, 2) 2
Lisbona model selection: 59%|█████████████████████████████████▊ | 16/27 [00:04<00:02, 3.92it/s]
1 Order: (1, 1, 2), AICc: 3016.25, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25 (1, 2, 0) 2 1 Order: (1, 2, 0), AICc: 3429.09, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25 (1, 2, 1) 2
Lisbona model selection: 63%|███████████████████████████████████▉ | 17/27 [00:05<00:02, 3.34it/s]
1 Order: (1, 2, 1), AICc: 3095.28, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25 (1, 2, 2) 2
Lisbona model selection: 67%|██████████████████████████████████████ | 18/27 [00:05<00:03, 2.75it/s]
1 Order: (1, 2, 2), AICc: 3082.57, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25 (2, 0, 0) 2
Lisbona model selection: 70%|████████████████████████████████████████ | 19/27 [00:06<00:03, 2.47it/s]
1 Order: (2, 0, 0), AICc: 3076.60, ADF p-value: 0.0136, Best_Order: (1, 1, 2), Best_aicc: 3016.25 (2, 0, 1) 2
Lisbona model selection: 74%|██████████████████████████████████████████▏ | 20/27 [00:06<00:03, 2.01it/s]
1 Order: (2, 0, 1), AICc: 3195.75, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25 (2, 0, 2) 2
Lisbona model selection: 78%|████████████████████████████████████████████▎ | 21/27 [00:07<00:03, 1.80it/s]
1 Order: (2, 0, 2), AICc: 3225.54, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25 (2, 1, 0) 2 1 Order: (2, 1, 0), AICc: 3057.07, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25
Lisbona model selection: 81%|██████████████████████████████████████████████▍ | 22/27 [00:07<00:02, 2.25it/s]
(2, 1, 1) 2
Lisbona model selection: 85%|████████████████████████████████████████████████▌ | 23/27 [00:08<00:01, 2.52it/s]
1 Order: (2, 1, 1), AICc: 3019.96, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25 (2, 1, 2) 2
Lisbona model selection: 89%|██████████████████████████████████████████████████▋ | 24/27 [00:08<00:01, 2.52it/s]
1 Order: (2, 1, 2), AICc: 3017.68, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25 (2, 2, 0) 2 1
Lisbona model selection: 93%|████████████████████████████████████████████████████▊ | 25/27 [00:08<00:00, 2.95it/s]
Order: (2, 2, 0), AICc: 3342.07, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25 (2, 2, 1) 2
Lisbona model selection: 96%|██████████████████████████████████████████████████████▉ | 26/27 [00:08<00:00, 2.98it/s]
1 Order: (2, 2, 1), AICc: 3061.72, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25 (2, 2, 2) 2
Lisbona model selection: 100%|█████████████████████████████████████████████████████████| 27/27 [00:09<00:00, 2.80it/s]
1 Order: (2, 2, 2), AICc: 3021.59, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25
Analysed test_samples from: 0%| | 0/70 [00:00<?, ?it/s]
1
Analysed test_samples from: 1%|▊ | 1/70 [00:00<00:17, 3.98it/s]
55.638533504210656 1
Analysed test_samples from: 3%|█▌ | 2/70 [00:00<00:17, 3.93it/s]
54.34913602586294 1
Analysed test_samples from: 4%|██▎ | 3/70 [00:00<00:17, 3.91it/s]
55.581947377416775 1
Analysed test_samples from: 6%|███▏ | 4/70 [00:01<00:17, 3.78it/s]
54.2796588053433 1
Analysed test_samples from: 7%|███▉ | 5/70 [00:01<00:17, 3.69it/s]
54.83680029491033 1
Analysed test_samples from: 9%|████▋ | 6/70 [00:01<00:17, 3.62it/s]
56.554787309373154 1
Analysed test_samples from: 10%|█████▌ | 7/70 [00:01<00:17, 3.69it/s]
57.00424405027884 1
Analysed test_samples from: 11%|██████▎ | 8/70 [00:02<00:16, 3.80it/s]
57.474946931149795 1
Analysed test_samples from: 13%|███████ | 9/70 [00:02<00:15, 3.82it/s]
56.780300394964776 1
Analysed test_samples from: 14%|███████▋ | 10/70 [00:02<00:15, 3.80it/s]
57.13698042825364 1
Analysed test_samples from: 16%|████████▍ | 11/70 [00:02<00:15, 3.89it/s]
56.143105598330386 1
Analysed test_samples from: 17%|█████████▎ | 12/70 [00:03<00:14, 3.96it/s]
56.177022648277834 1
Analysed test_samples from: 19%|██████████ | 13/70 [00:03<00:14, 4.02it/s]
56.76094274411291 1
Analysed test_samples from: 20%|██████████▊ | 14/70 [00:03<00:13, 4.01it/s]
56.930625070022714 1
Analysed test_samples from: 21%|███████████▌ | 15/70 [00:03<00:13, 3.97it/s]
56.23478676507199 1
Analysed test_samples from: 23%|████████████▎ | 16/70 [00:04<00:13, 3.98it/s]
55.47108597816368 1
Analysed test_samples from: 24%|█████████████ | 17/70 [00:04<00:12, 4.08it/s]
56.31961238613876 1
Analysed test_samples from: 26%|█████████████▉ | 18/70 [00:04<00:12, 4.07it/s]
56.83885001295771 1
Analysed test_samples from: 27%|██████████████▋ | 19/70 [00:04<00:12, 4.07it/s]
57.30344138029423 1
Analysed test_samples from: 29%|███████████████▍ | 20/70 [00:05<00:12, 3.96it/s]
57.324873503656335 1
Analysed test_samples from: 30%|████████████████▏ | 21/70 [00:05<00:12, 3.87it/s]
57.466400663936895 1
Analysed test_samples from: 31%|████████████████▉ | 22/70 [00:05<00:12, 3.88it/s]
56.805448534775266 1
Analysed test_samples from: 33%|█████████████████▋ | 23/70 [00:05<00:12, 3.83it/s]
57.11389813321358 1
Analysed test_samples from: 34%|██████████████████▌ | 24/70 [00:06<00:12, 3.78it/s]
56.79512934365613 1
Analysed test_samples from: 36%|███████████████████▎ | 25/70 [00:06<00:12, 3.67it/s]
56.93088445148352 1
Analysed test_samples from: 37%|████████████████████ | 26/70 [00:06<00:11, 3.67it/s]
57.08361411766375 1
Analysed test_samples from: 39%|████████████████████▊ | 27/70 [00:07<00:11, 3.62it/s]
57.42941204706231 1
Analysed test_samples from: 40%|█████████████████████▌ | 28/70 [00:07<00:11, 3.69it/s]
56.540571636158276 1
Analysed test_samples from: 41%|██████████████████████▎ | 29/70 [00:07<00:10, 3.79it/s]
56.681377151938456 1
Analysed test_samples from: 43%|███████████████████████▏ | 30/70 [00:07<00:10, 3.76it/s]
55.634026456029716 1
Analysed test_samples from: 44%|███████████████████████▉ | 31/70 [00:08<00:10, 3.79it/s]
54.39265484712358 1
Analysed test_samples from: 46%|████████████████████████▋ | 32/70 [00:08<00:10, 3.73it/s]
53.696869498821115 1
Analysed test_samples from: 47%|█████████████████████████▍ | 33/70 [00:08<00:09, 3.72it/s]
53.78178168325519 1
Analysed test_samples from: 49%|██████████████████████████▏ | 34/70 [00:08<00:09, 3.70it/s]
55.49835638682394 1
Analysed test_samples from: 50%|███████████████████████████ | 35/70 [00:09<00:09, 3.67it/s]
56.08978612097363 1
Analysed test_samples from: 51%|███████████████████████████▊ | 36/70 [00:09<00:09, 3.68it/s]
56.46314157245405 1
Analysed test_samples from: 53%|████████████████████████████▌ | 37/70 [00:09<00:09, 3.46it/s]
57.17594634449375 1
Analysed test_samples from: 54%|█████████████████████████████▎ | 38/70 [00:10<00:09, 3.42it/s]
58.08038471927539 1
Analysed test_samples from: 56%|██████████████████████████████ | 39/70 [00:10<00:09, 3.37it/s]
57.54924819642701 1
Analysed test_samples from: 57%|██████████████████████████████▊ | 40/70 [00:10<00:08, 3.36it/s]
57.49833473668315 1
Analysed test_samples from: 59%|███████████████████████████████▋ | 41/70 [00:10<00:08, 3.41it/s]
57.1653254813356 1
Analysed test_samples from: 60%|████████████████████████████████▍ | 42/70 [00:11<00:08, 3.50it/s]
57.141995033961166 1
Analysed test_samples from: 61%|█████████████████████████████████▏ | 43/70 [00:11<00:07, 3.51it/s]
57.379583358215875 1
Analysed test_samples from: 63%|█████████████████████████████████▉ | 44/70 [00:11<00:07, 3.53it/s]
57.41352950418796 1
Analysed test_samples from: 64%|██████████████████████████████████▋ | 45/70 [00:12<00:07, 3.51it/s]
57.24382461324576 1
Analysed test_samples from: 66%|███████████████████████████████████▍ | 46/70 [00:12<00:06, 3.47it/s]
57.09108370784904 1
Analysed test_samples from: 67%|████████████████████████████████████▎ | 47/70 [00:12<00:06, 3.43it/s]
56.93834461757357 1
Analysed test_samples from: 69%|█████████████████████████████████████ | 48/70 [00:12<00:06, 3.43it/s]
55.648589145313146 1
Analysed test_samples from: 70%|█████████████████████████████████████▊ | 49/70 [00:13<00:06, 3.27it/s]
55.66555281402784 1
Analysed test_samples from: 71%|██████████████████████████████████████▌ | 50/70 [00:13<00:06, 3.27it/s]
56.734692586724194 1
Analysed test_samples from: 73%|███████████████████████████████████████▎ | 51/70 [00:13<00:05, 3.28it/s]
56.84949887695948 1
Analysed test_samples from: 74%|████████████████████████████████████████ | 52/70 [00:14<00:05, 3.30it/s]
56.60551274991194 1
Analysed test_samples from: 76%|████████████████████████████████████████▉ | 53/70 [00:14<00:05, 3.31it/s]
57.05712610010603 1
Analysed test_samples from: 77%|█████████████████████████████████████████▋ | 54/70 [00:14<00:04, 3.31it/s]
57.20985894896014 1
Analysed test_samples from: 79%|██████████████████████████████████████████▍ | 55/70 [00:15<00:04, 3.31it/s]
57.82835331767006 1
Analysed test_samples from: 80%|███████████████████████████████████████████▏ | 56/70 [00:15<00:04, 3.20it/s]
57.294699655871824 1
Analysed test_samples from: 81%|███████████████████████████████████████████▉ | 57/70 [00:15<00:04, 3.11it/s]
57.14196677778301 1
Analysed test_samples from: 83%|████████████████████████████████████████████▋ | 58/70 [00:16<00:03, 3.09it/s]
56.95529618371146 1
Analysed test_samples from: 84%|█████████████████████████████████████████████▌ | 59/70 [00:16<00:03, 3.11it/s]
56.93832742628798 1
Analysed test_samples from: 86%|██████████████████████████████████████████████▎ | 60/70 [00:16<00:03, 3.09it/s]
57.115466894582184 1
Analysed test_samples from: 87%|███████████████████████████████████████████████ | 61/70 [00:17<00:02, 3.10it/s]
57.35304371577831 1
Analysed test_samples from: 89%|███████████████████████████████████████████████▊ | 62/70 [00:17<00:02, 3.00it/s]
57.12052445897405 1
Analysed test_samples from: 90%|████████████████████████████████████████████████▌ | 63/70 [00:17<00:02, 3.02it/s]
56.91186325923353 1
Analysed test_samples from: 91%|█████████████████████████████████████████████████▎ | 64/70 [00:18<00:01, 3.02it/s]
57.09852498931692 1
Analysed test_samples from: 93%|██████████████████████████████████████████████████▏ | 65/70 [00:18<00:01, 3.06it/s]
56.79309402059438 1
Analysed test_samples from: 94%|██████████████████████████████████████████████████▉ | 66/70 [00:18<00:01, 3.18it/s]
56.7761657519561 1
Analysed test_samples from: 96%|███████████████████████████████████████████████████▋ | 67/70 [00:19<00:00, 3.22it/s]
56.301025572563645 1
Analysed test_samples from: 97%|████████████████████████████████████████████████████▍ | 68/70 [00:19<00:00, 3.19it/s]
56.89494198862489 1
Analysed test_samples from: 99%|█████████████████████████████████████████████████████▏| 69/70 [00:19<00:00, 3.25it/s]
57.30224965775684 1
Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 70/70 [00:19<00:00, 3.51it/s]
56.419846653620056 False False
Londyn model selection: 0%| | 0/27 [00:00<?, ?it/s]
(0, 0, 0) 2
Londyn model selection: 4%|██▏ | 1/27 [00:00<00:08, 3.12it/s]
1 Order: (0, 0, 0), AICc: 3531.24, ADF p-value: 0.0000, Best_Order: (0, 0, 0), Best_aicc: 3531.24 (0, 0, 1) 2
Londyn model selection: 7%|████▎ | 2/27 [00:00<00:07, 3.31it/s]
1 Order: (0, 0, 1), AICc: 3247.42, ADF p-value: 0.0000, Best_Order: (0, 0, 1), Best_aicc: 3247.42 (0, 0, 2) 2
Londyn model selection: 11%|██████▌ | 3/27 [00:00<00:07, 3.00it/s]
1 Order: (0, 0, 2), AICc: 3147.15, ADF p-value: 0.0000, Best_Order: (0, 0, 2), Best_aicc: 3147.15 (0, 1, 0) 2 1
Londyn model selection: 15%|████████▋ | 4/27 [00:01<00:06, 3.55it/s]
Order: (0, 1, 0), AICc: 2978.23, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 2978.23 (0, 1, 1) 2 1 Order: (0, 1, 1), AICc: 2970.09, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 2970.09
Londyn model selection: 22%|█████████████ | 6/27 [00:01<00:04, 4.32it/s]
(0, 1, 2) 2 1 Order: (0, 1, 2), AICc: 2913.23, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23
Londyn model selection: 26%|███████████████▎ | 7/27 [00:01<00:04, 4.61it/s]
(0, 2, 0) 2 1 Order: (0, 2, 0), AICc: 3456.92, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23 (0, 2, 1) 2
Londyn model selection: 30%|█████████████████▍ | 8/27 [00:02<00:05, 3.65it/s]
1 Order: (0, 2, 1), AICc: 2977.13, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23 (0, 2, 2) 2
Londyn model selection: 33%|███████████████████▋ | 9/27 [00:02<00:06, 2.87it/s]
1 Order: (0, 2, 2), AICc: 2970.76, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23 (1, 0, 0) 2
Londyn model selection: 37%|█████████████████████▍ | 10/27 [00:03<00:05, 2.90it/s]
1 Order: (1, 0, 0), AICc: 2981.98, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23 (1, 0, 1) 2
Londyn model selection: 41%|███████████████████████▋ | 11/27 [00:03<00:05, 2.85it/s]
1 Order: (1, 0, 1), AICc: 2975.01, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23 (1, 0, 2) 2
Londyn model selection: 44%|█████████████████████████▊ | 12/27 [00:03<00:06, 2.37it/s]
1 Order: (1, 0, 2), AICc: 2918.61, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23 (1, 1, 0) 2
Londyn model selection: 48%|███████████████████████████▉ | 13/27 [00:04<00:04, 2.80it/s]
1 Order: (1, 1, 0), AICc: 2977.24, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23 (1, 1, 1) 2
Londyn model selection: 52%|██████████████████████████████ | 14/27 [00:04<00:04, 2.74it/s]
1 Order: (1, 1, 1), AICc: 2904.19, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 2904.19 (1, 1, 2) 2
Londyn model selection: 56%|████████████████████████████████▏ | 15/27 [00:04<00:04, 2.72it/s]
1 Order: (1, 1, 2), AICc: 2891.63, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63 (1, 2, 0) 2
Londyn model selection: 59%|██████████████████████████████████▎ | 16/27 [00:05<00:03, 2.88it/s]
1 Order: (1, 2, 0), AICc: 3325.44, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63 (1, 2, 1) 2
Londyn model selection: 63%|████████████████████████████████████▌ | 17/27 [00:05<00:03, 2.70it/s]
1 Order: (1, 2, 1), AICc: 2977.37, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63 (1, 2, 2) 2
Londyn model selection: 67%|██████████████████████████████████████▋ | 18/27 [00:06<00:03, 2.49it/s]
1 Order: (1, 2, 2), AICc: 2977.02, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63 (2, 0, 0) 2
Londyn model selection: 70%|████████████████████████████████████████▊ | 19/27 [00:06<00:03, 2.45it/s]
1 Order: (2, 0, 0), AICc: 2976.98, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63 (2, 0, 1) 2
Londyn model selection: 74%|██████████████████████████████████████████▉ | 20/27 [00:07<00:03, 2.14it/s]
1 Order: (2, 0, 1), AICc: 3001.97, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63 (2, 0, 2) 2
Londyn model selection: 78%|█████████████████████████████████████████████ | 21/27 [00:07<00:03, 1.91it/s]
1 Order: (2, 0, 2), AICc: 2996.36, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63 (2, 1, 0) 2 1
Londyn model selection: 81%|███████████████████████████████████████████████▎ | 22/27 [00:08<00:02, 2.33it/s]
Order: (2, 1, 0), AICc: 2947.42, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63 (2, 1, 1) 2
Londyn model selection: 85%|█████████████████████████████████████████████████▍ | 23/27 [00:08<00:01, 2.51it/s]
1 Order: (2, 1, 1), AICc: 2895.85, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63 (2, 1, 2) 2
Londyn model selection: 89%|███████████████████████████████████████████████████▌ | 24/27 [00:08<00:01, 2.62it/s]
1 Order: (2, 1, 2), AICc: 2893.32, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63 (2, 2, 0) 2
Londyn model selection: 93%|█████████████████████████████████████████████████████▋ | 25/27 [00:08<00:00, 2.83it/s]
1 Order: (2, 2, 0), AICc: 3240.10, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63 (2, 2, 1) 2
Londyn model selection: 96%|███████████████████████████████████████████████████████▊ | 26/27 [00:09<00:00, 2.55it/s]
1 Order: (2, 2, 1), AICc: 2952.40, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63 (2, 2, 2) 2
Londyn model selection: 100%|██████████████████████████████████████████████████████████| 27/27 [00:10<00:00, 2.64it/s]
1 Order: (2, 2, 2), AICc: 2901.26, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
Analysed test_samples from: 0%| | 0/72 [00:00<?, ?it/s]
1
Analysed test_samples from: 1%|▊ | 1/72 [00:00<00:23, 3.07it/s]
53.700043525275596 1
Analysed test_samples from: 3%|█▌ | 2/72 [00:00<00:24, 2.89it/s]
52.128507687567506 1
Analysed test_samples from: 4%|██▎ | 3/72 [00:01<00:24, 2.81it/s]
53.53918838468976 1
Analysed test_samples from: 6%|███ | 4/72 [00:01<00:21, 3.20it/s]
52.35377654490019 1
Analysed test_samples from: 7%|███▊ | 5/72 [00:01<00:19, 3.48it/s]
51.746621572936064 1
Analysed test_samples from: 8%|████▌ | 6/72 [00:01<00:21, 3.02it/s]
53.487163908133965 1
Analysed test_samples from: 10%|█████▎ | 7/72 [00:02<00:22, 2.90it/s]
53.03800287357925 1
Analysed test_samples from: 11%|██████ | 8/72 [00:02<00:19, 3.22it/s]
54.00379146973812 1
Analysed test_samples from: 12%|██████▉ | 9/72 [00:02<00:18, 3.36it/s]
52.31339117579628 1
Analysed test_samples from: 14%|███████▌ | 10/72 [00:03<00:19, 3.24it/s]
52.310426618758115 1
Analysed test_samples from: 15%|████████▎ | 11/72 [00:03<00:18, 3.23it/s]
53.68333454251308 1
Analysed test_samples from: 17%|█████████ | 12/72 [00:03<00:19, 3.12it/s]
52.7681885200096 1
Analysed test_samples from: 18%|█████████▊ | 13/72 [00:04<00:18, 3.22it/s]
50.46784121817875 1
Analysed test_samples from: 19%|██████████▌ | 14/72 [00:04<00:17, 3.28it/s]
50.491993637327695 1
Analysed test_samples from: 21%|███████████▎ | 15/72 [00:04<00:16, 3.38it/s]
49.9344476607515 1
Analysed test_samples from: 22%|████████████ | 16/72 [00:04<00:16, 3.42it/s]
50.79100638119145 1
Analysed test_samples from: 24%|████████████▊ | 17/72 [00:05<00:15, 3.60it/s]
49.97866797190333 1
Analysed test_samples from: 25%|█████████████▌ | 18/72 [00:05<00:14, 3.67it/s]
49.93279515215053 1
Analysed test_samples from: 26%|██████████████▎ | 19/72 [00:05<00:14, 3.67it/s]
50.487365395534 1
Analysed test_samples from: 28%|███████████████ | 20/72 [00:06<00:14, 3.71it/s]
48.490592019215754 1
Analysed test_samples from: 29%|███████████████▊ | 21/72 [00:06<00:13, 3.70it/s]
48.341795820833795 1
Analysed test_samples from: 31%|████████████████▌ | 22/72 [00:06<00:13, 3.70it/s]
47.665369471405114 1
Analysed test_samples from: 32%|█████████████████▎ | 23/72 [00:06<00:12, 3.79it/s]
49.90286331609413 1
Analysed test_samples from: 33%|██████████████████ | 24/72 [00:07<00:12, 3.72it/s]
51.746959805386325 1
Analysed test_samples from: 35%|██████████████████▊ | 25/72 [00:07<00:12, 3.70it/s]
51.34489200280452 1
Analysed test_samples from: 36%|███████████████████▌ | 26/72 [00:07<00:12, 3.67it/s]
49.27826461388156 1
Analysed test_samples from: 38%|████████████████████▎ | 27/72 [00:07<00:12, 3.65it/s]
46.4605339552424 1
Analysed test_samples from: 39%|█████████████████████ | 28/72 [00:08<00:12, 3.62it/s]
47.95628874844493 1
Analysed test_samples from: 40%|█████████████████████▊ | 29/72 [00:08<00:11, 3.61it/s]
53.56417949204265 1
Analysed test_samples from: 42%|██████████████████████▌ | 30/72 [00:08<00:11, 3.64it/s]
51.64631315907371 1
Analysed test_samples from: 43%|███████████████████████▎ | 31/72 [00:09<00:11, 3.64it/s]
50.91423062183446 1
Analysed test_samples from: 44%|████████████████████████ | 32/72 [00:09<00:10, 3.65it/s]
50.98927170052613 1
Analysed test_samples from: 46%|████████████████████████▊ | 33/72 [00:09<00:10, 3.67it/s]
48.729831274915355 1
Analysed test_samples from: 47%|█████████████████████████▌ | 34/72 [00:09<00:10, 3.61it/s]
52.90933255062059 1
Analysed test_samples from: 49%|██████████████████████████▎ | 35/72 [00:10<00:10, 3.54it/s]
44.833699823752355 1
Analysed test_samples from: 50%|███████████████████████████ | 36/72 [00:10<00:10, 3.58it/s]
40.191641441204226 1
Analysed test_samples from: 51%|███████████████████████████▋ | 37/72 [00:10<00:09, 3.59it/s]
43.49328744124696 1
Analysed test_samples from: 53%|████████████████████████████▌ | 38/72 [00:10<00:09, 3.66it/s]
49.11095497936694 1
Analysed test_samples from: 54%|█████████████████████████████▏ | 39/72 [00:11<00:08, 3.75it/s]
45.00505099955254 1
Analysed test_samples from: 56%|██████████████████████████████ | 40/72 [00:11<00:09, 3.37it/s]
41.707761754281115 1
Analysed test_samples from: 57%|██████████████████████████████▊ | 41/72 [00:11<00:09, 3.21it/s]
39.191946516891136 1
Analysed test_samples from: 58%|███████████████████████████████▌ | 42/72 [00:12<00:09, 3.05it/s]
38.288667040632426 1
Analysed test_samples from: 60%|████████████████████████████████▎ | 43/72 [00:12<00:08, 3.24it/s]
37.868587392876 1
Analysed test_samples from: 61%|█████████████████████████████████ | 44/72 [00:12<00:08, 3.16it/s]
45.85076541164648 1
Analysed test_samples from: 62%|█████████████████████████████████▊ | 45/72 [00:13<00:08, 3.26it/s]
46.22620817195936 1
Analysed test_samples from: 64%|██████████████████████████████████▌ | 46/72 [00:13<00:07, 3.35it/s]
45.51218617383205 1
Analysed test_samples from: 65%|███████████████████████████████████▎ | 47/72 [00:13<00:07, 3.40it/s]
43.891413699276185 1
Analysed test_samples from: 67%|████████████████████████████████████ | 48/72 [00:14<00:07, 3.13it/s]
46.64817171204582 1
Analysed test_samples from: 68%|████████████████████████████████████▊ | 49/72 [00:14<00:07, 3.27it/s]
48.89402277093959 1
Analysed test_samples from: 69%|█████████████████████████████████████▌ | 50/72 [00:14<00:06, 3.27it/s]
49.627645195047776 1
Analysed test_samples from: 71%|██████████████████████████████████████▎ | 51/72 [00:14<00:06, 3.37it/s]
49.21052560504836 1
Analysed test_samples from: 72%|███████████████████████████████████████ | 52/72 [00:15<00:05, 3.49it/s]
49.370924713732194 1
Analysed test_samples from: 74%|███████████████████████████████████████▊ | 53/72 [00:15<00:05, 3.25it/s]
51.256687660737 1
Analysed test_samples from: 75%|████████████████████████████████████████▌ | 54/72 [00:15<00:06, 3.00it/s]
47.82868295651171 1
Analysed test_samples from: 76%|█████████████████████████████████████████▎ | 55/72 [00:16<00:05, 3.15it/s]
45.91721955803389 1
Analysed test_samples from: 78%|██████████████████████████████████████████ | 56/72 [00:16<00:05, 3.08it/s]
46.53768062389028 1
Analysed test_samples from: 79%|██████████████████████████████████████████▊ | 57/72 [00:16<00:05, 2.93it/s]
51.00293851896077 1
Analysed test_samples from: 81%|███████████████████████████████████████████▌ | 58/72 [00:17<00:04, 2.90it/s]
50.032123240027886 1
Analysed test_samples from: 82%|████████████████████████████████████████████▎ | 59/72 [00:17<00:04, 3.15it/s]
51.11488389619551 1
Analysed test_samples from: 83%|█████████████████████████████████████████████ | 60/72 [00:17<00:03, 3.27it/s]
50.84511096396315 1
Analysed test_samples from: 85%|█████████████████████████████████████████████▊ | 61/72 [00:18<00:03, 3.34it/s]
49.16440782718508 1
Analysed test_samples from: 86%|██████████████████████████████████████████████▌ | 62/72 [00:18<00:03, 3.09it/s]
51.80241246433608 1
Analysed test_samples from: 88%|███████████████████████████████████████████████▎ | 63/72 [00:18<00:02, 3.01it/s]
50.754896851393795 1
Analysed test_samples from: 89%|████████████████████████████████████████████████ | 64/72 [00:19<00:02, 2.93it/s]
52.227575757606246 1
Analysed test_samples from: 90%|████████████████████████████████████████████████▊ | 65/72 [00:19<00:02, 3.11it/s]
56.44374137895359 1
Analysed test_samples from: 92%|█████████████████████████████████████████████████▌ | 66/72 [00:19<00:01, 3.19it/s]
54.92519439958987 1
Analysed test_samples from: 93%|██████████████████████████████████████████████████▎ | 67/72 [00:20<00:01, 3.29it/s]
47.32231620660458 1
Analysed test_samples from: 94%|███████████████████████████████████████████████████ | 68/72 [00:20<00:01, 3.34it/s]
50.66854982755782 1
Analysed test_samples from: 96%|███████████████████████████████████████████████████▊ | 69/72 [00:20<00:00, 3.33it/s]
49.90038885031902 1
Analysed test_samples from: 97%|████████████████████████████████████████████████████▌ | 70/72 [00:20<00:00, 3.37it/s]
49.64257022854768 1
Analysed test_samples from: 99%|█████████████████████████████████████████████████████▎| 71/72 [00:21<00:00, 3.35it/s]
50.12571523285525 1
Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 72/72 [00:21<00:00, 3.34it/s]
48.21790834734276 False False
Madryd model selection: 0%| | 0/27 [00:00<?, ?it/s]
(0, 0, 0) 2
Madryd model selection: 4%|██▏ | 1/27 [00:00<00:08, 3.08it/s]
1 Order: (0, 0, 0), AICc: 5095.04, ADF p-value: 0.1031, Best_Order: (0, 0, 0), Best_aicc: inf (0, 0, 1) 2
Madryd model selection: 7%|████▎ | 2/27 [00:00<00:06, 3.77it/s]
1 Order: (0, 0, 1), AICc: 4498.82, ADF p-value: 0.0997, Best_Order: (0, 0, 0), Best_aicc: inf (0, 0, 2) 2
Madryd model selection: 15%|████████▋ | 4/27 [00:00<00:05, 4.39it/s]
1 Order: (0, 0, 2), AICc: 4223.54, ADF p-value: 0.1386, Best_Order: (0, 0, 0), Best_aicc: inf (0, 1, 0) 2 1 Order: (0, 1, 0), AICc: 3374.05, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3374.05 (0, 1, 1) 2
Madryd model selection: 19%|██████████▉ | 5/27 [00:01<00:04, 4.75it/s]
1 Order: (0, 1, 1), AICc: 3371.65, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3371.65 (0, 1, 2) 2 1 Order: (0, 1, 2), AICc: 3345.93, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93
Madryd model selection: 22%|█████████████ | 6/27 [00:01<00:04, 5.09it/s]
(0, 2, 0) 2
Madryd model selection: 26%|███████████████▎ | 7/27 [00:01<00:04, 4.81it/s]
1 Order: (0, 2, 0), AICc: 3810.48, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93 (0, 2, 1) 2
Madryd model selection: 30%|█████████████████▍ | 8/27 [00:01<00:04, 4.23it/s]
1 Order: (0, 2, 1), AICc: 3373.02, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93 (0, 2, 2) 2
Madryd model selection: 33%|███████████████████▋ | 9/27 [00:02<00:05, 3.03it/s]
1 Order: (0, 2, 2), AICc: 3370.80, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93 (1, 0, 0) 2
Madryd model selection: 37%|█████████████████████▍ | 10/27 [00:02<00:05, 3.11it/s]
1 Order: (1, 0, 0), AICc: 3379.75, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93 (1, 0, 1) 2
Madryd model selection: 41%|███████████████████████▋ | 11/27 [00:03<00:05, 2.81it/s]
1 Order: (1, 0, 1), AICc: 3377.52, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93 (1, 0, 2) 2
Madryd model selection: 48%|███████████████████████████▉ | 13/27 [00:03<00:04, 2.95it/s]
1 Order: (1, 0, 2), AICc: 3352.60, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93 (1, 1, 0) 2 1 Order: (1, 1, 0), AICc: 3376.00, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93 (1, 1, 1) 2
Madryd model selection: 52%|██████████████████████████████ | 14/27 [00:04<00:04, 2.95it/s]
1 Order: (1, 1, 1), AICc: 3349.29, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93 (1, 1, 2) 2
Madryd model selection: 56%|████████████████████████████████▏ | 15/27 [00:04<00:04, 2.99it/s]
1 Order: (1, 1, 2), AICc: 3331.21, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21 (1, 2, 0) 2 1 Order: (1, 2, 0), AICc: 3695.81, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
Madryd model selection: 59%|██████████████████████████████████▎ | 16/27 [00:04<00:03, 3.47it/s]
(1, 2, 1) 2
Madryd model selection: 63%|████████████████████████████████████▌ | 17/27 [00:05<00:03, 2.89it/s]
1 Order: (1, 2, 1), AICc: 3375.06, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21 (1, 2, 2) 2
Madryd model selection: 67%|██████████████████████████████████████▋ | 18/27 [00:05<00:04, 2.24it/s]
1 Order: (1, 2, 2), AICc: 3368.84, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21 (2, 0, 0) 2
Madryd model selection: 70%|████████████████████████████████████████▊ | 19/27 [00:06<00:03, 2.22it/s]
1 Order: (2, 0, 0), AICc: 3377.58, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21 (2, 0, 1) 2
Madryd model selection: 74%|██████████████████████████████████████████▉ | 20/27 [00:06<00:03, 2.03it/s]
1 Order: (2, 0, 1), AICc: 3549.70, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21 (2, 0, 2) 2
Madryd model selection: 78%|█████████████████████████████████████████████ | 21/27 [00:07<00:03, 1.84it/s]
1 Order: (2, 0, 2), AICc: 3338.69, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21 (2, 1, 0) 2
Madryd model selection: 81%|███████████████████████████████████████████████▎ | 22/27 [00:07<00:02, 2.24it/s]
1 Order: (2, 1, 0), AICc: 3355.02, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21 (2, 1, 1) 2
Madryd model selection: 85%|█████████████████████████████████████████████████▍ | 23/27 [00:08<00:01, 2.52it/s]
1 Order: (2, 1, 1), AICc: 3335.71, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21 (2, 1, 2) 2
Madryd model selection: 89%|███████████████████████████████████████████████████▌ | 24/27 [00:08<00:01, 2.12it/s]
1 Order: (2, 1, 2), AICc: 3333.00, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21 (2, 2, 0) 2
Madryd model selection: 93%|█████████████████████████████████████████████████████▋ | 25/27 [00:08<00:00, 2.49it/s]
1 Order: (2, 2, 0), AICc: 3607.15, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21 (2, 2, 1) 2
Madryd model selection: 96%|███████████████████████████████████████████████████████▊ | 26/27 [00:09<00:00, 2.39it/s]
1 Order: (2, 2, 1), AICc: 3359.43, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21 (2, 2, 2) 2
Madryd model selection: 100%|██████████████████████████████████████████████████████████| 27/27 [00:10<00:00, 2.66it/s]
1 Order: (2, 2, 2), AICc: 3336.45, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
Analysed test_samples from: 0%| | 0/73 [00:00<?, ?it/s]
1
Analysed test_samples from: 1%|▊ | 1/73 [00:00<00:21, 3.29it/s]
56.84418113471926 1
Analysed test_samples from: 3%|█▌ | 2/73 [00:00<00:23, 3.01it/s]
57.16715138817983 1
Analysed test_samples from: 4%|██▎ | 3/73 [00:00<00:23, 3.02it/s]
59.097220624308875 1
Analysed test_samples from: 5%|███ | 4/73 [00:01<00:21, 3.27it/s]
60.75379985784121 1
Analysed test_samples from: 7%|███▊ | 5/73 [00:01<00:22, 2.98it/s]
60.1257459276806 1
Analysed test_samples from: 8%|████▌ | 6/73 [00:01<00:22, 3.00it/s]
62.14833183493873 1
Analysed test_samples from: 10%|█████▎ | 7/73 [00:02<00:20, 3.26it/s]
62.47077017817846 1
Analysed test_samples from: 11%|██████ | 8/73 [00:02<00:20, 3.17it/s]
60.624302400465396 1
Analysed test_samples from: 12%|██████▊ | 9/73 [00:02<00:20, 3.07it/s]
61.19734856503432 1
Analysed test_samples from: 14%|███████▍ | 10/73 [00:03<00:19, 3.15it/s]
61.6304267640063 1
Analysed test_samples from: 15%|████████▏ | 11/73 [00:03<00:19, 3.19it/s]
60.356623087134395 1
Analysed test_samples from: 16%|████████▉ | 12/73 [00:03<00:19, 3.07it/s]
59.663081812355436 1
Analysed test_samples from: 18%|█████████▌ | 13/73 [00:04<00:18, 3.30it/s]
60.99623026795676 1
Analysed test_samples from: 19%|██████████▎ | 14/73 [00:04<00:16, 3.50it/s]
60.022173693887396 1
Analysed test_samples from: 21%|███████████ | 15/73 [00:04<00:17, 3.28it/s]
59.22142439365415 1
Analysed test_samples from: 22%|███████████▊ | 16/73 [00:05<00:17, 3.18it/s]
60.956410329595734 1
Analysed test_samples from: 23%|████████████▌ | 17/73 [00:05<00:17, 3.15it/s]
59.35920481809927 1
Analysed test_samples from: 25%|█████████████▎ | 18/73 [00:05<00:17, 3.08it/s]
57.897130293141586 1
Analysed test_samples from: 26%|██████████████ | 19/73 [00:06<00:18, 2.98it/s]
57.91181655449001 1
Analysed test_samples from: 27%|██████████████▊ | 20/73 [00:06<00:18, 2.92it/s]
58.57593007465846 1
Analysed test_samples from: 29%|███████████████▌ | 21/73 [00:06<00:17, 2.95it/s]
59.740732692310324 1
Analysed test_samples from: 30%|████████████████▎ | 22/73 [00:07<00:17, 2.85it/s]
59.2066686689232 1
Analysed test_samples from: 32%|█████████████████ | 23/73 [00:07<00:17, 2.81it/s]
62.29815404060058 1
Analysed test_samples from: 33%|█████████████████▊ | 24/73 [00:07<00:17, 2.82it/s]
61.96870036579729 1
Analysed test_samples from: 34%|██████████████████▍ | 25/73 [00:08<00:16, 2.84it/s]
62.467167621348054 1
Analysed test_samples from: 36%|███████████████████▏ | 26/73 [00:08<00:16, 2.84it/s]
61.18361314470391 1
Analysed test_samples from: 37%|███████████████████▉ | 27/73 [00:08<00:16, 2.80it/s]
60.99303884735693 1
Analysed test_samples from: 38%|████████████████████▋ | 28/73 [00:09<00:16, 2.81it/s]
60.877802337963054 1
Analysed test_samples from: 40%|█████████████████████▍ | 29/73 [00:09<00:15, 2.83it/s]
61.336218837757926 1
Analysed test_samples from: 41%|██████████████████████▏ | 30/73 [00:10<00:15, 2.78it/s]
60.18423355878912 1
Analysed test_samples from: 42%|██████████████████████▉ | 31/73 [00:10<00:15, 2.75it/s]
59.87224579037867 1
Analysed test_samples from: 44%|███████████████████████▋ | 32/73 [00:10<00:14, 2.75it/s]
59.838667189775144 1
Analysed test_samples from: 45%|████████████████████████▍ | 33/73 [00:11<00:14, 2.78it/s]
57.884706277835214 1
Analysed test_samples from: 47%|█████████████████████████▏ | 34/73 [00:11<00:13, 2.85it/s]
57.61254346182554 1
Analysed test_samples from: 48%|█████████████████████████▉ | 35/73 [00:11<00:13, 2.79it/s]
55.46230265933541 1
Analysed test_samples from: 49%|██████████████████████████▋ | 36/73 [00:12<00:13, 2.75it/s]
56.756481137571754 1
Analysed test_samples from: 51%|███████████████████████████▎ | 37/73 [00:12<00:13, 2.77it/s]
57.52076132958234 1
Analysed test_samples from: 52%|████████████████████████████ | 38/73 [00:12<00:13, 2.69it/s]
57.56261898497799 1
Analysed test_samples from: 53%|████████████████████████████▊ | 39/73 [00:13<00:12, 2.72it/s]
58.45108601827787 1
Analysed test_samples from: 55%|█████████████████████████████▌ | 40/73 [00:13<00:12, 2.73it/s]
59.84931491851994 1
Analysed test_samples from: 56%|██████████████████████████████▎ | 41/73 [00:14<00:11, 2.73it/s]
61.32951749625459 1
Analysed test_samples from: 58%|███████████████████████████████ | 42/73 [00:14<00:11, 2.71it/s]
61.97453924171311 1
Analysed test_samples from: 59%|███████████████████████████████▊ | 43/73 [00:14<00:11, 2.71it/s]
58.31337645565895 1
Analysed test_samples from: 60%|████████████████████████████████▌ | 44/73 [00:15<00:10, 2.66it/s]
56.840174653998645 1
Analysed test_samples from: 62%|█████████████████████████████████▎ | 45/73 [00:15<00:10, 2.64it/s]
56.739566383010015 1
Analysed test_samples from: 63%|██████████████████████████████████ | 46/73 [00:15<00:10, 2.60it/s]
58.57676288092152 1
Analysed test_samples from: 64%|██████████████████████████████████▊ | 47/73 [00:16<00:09, 2.60it/s]
56.88614944998699 1
Analysed test_samples from: 66%|███████████████████████████████████▌ | 48/73 [00:16<00:09, 2.63it/s]
57.19377713085008 1
Analysed test_samples from: 67%|████████████████████████████████████▏ | 49/73 [00:17<00:09, 2.60it/s]
57.46232968474985 1
Analysed test_samples from: 68%|████████████████████████████████████▉ | 50/73 [00:17<00:08, 2.60it/s]
58.19531422264831 1
Analysed test_samples from: 70%|█████████████████████████████████████▋ | 51/73 [00:17<00:08, 2.61it/s]
59.198328672762656 1
Analysed test_samples from: 71%|██████████████████████████████████████▍ | 52/73 [00:18<00:08, 2.59it/s]
60.75800930543049 1
Analysed test_samples from: 73%|███████████████████████████████████████▏ | 53/73 [00:18<00:07, 2.59it/s]
60.39154788597341 1
Analysed test_samples from: 74%|███████████████████████████████████████▉ | 54/73 [00:19<00:07, 2.51it/s]
60.16961846270894 1
Analysed test_samples from: 75%|████████████████████████████████████████▋ | 55/73 [00:19<00:07, 2.53it/s]
58.26359698915964 1
Analysed test_samples from: 77%|█████████████████████████████████████████▍ | 56/73 [00:19<00:06, 2.55it/s]
56.785447633840896 1
Analysed test_samples from: 78%|██████████████████████████████████████████▏ | 57/73 [00:20<00:06, 2.54it/s]
57.683850947144265 1
Analysed test_samples from: 79%|██████████████████████████████████████████▉ | 58/73 [00:20<00:05, 2.51it/s]
56.377182361498654 1
Analysed test_samples from: 81%|███████████████████████████████████████████▋ | 59/73 [00:21<00:05, 2.51it/s]
56.25990709559484 1
Analysed test_samples from: 82%|████████████████████████████████████████████▍ | 60/73 [00:21<00:05, 2.51it/s]
56.042151058397835 1
Analysed test_samples from: 84%|█████████████████████████████████████████████ | 61/73 [00:21<00:04, 2.54it/s]
56.21599181112341 1
Analysed test_samples from: 85%|█████████████████████████████████████████████▊ | 62/73 [00:22<00:04, 2.55it/s]
56.49468781219472 1
Analysed test_samples from: 86%|██████████████████████████████████████████████▌ | 63/73 [00:22<00:03, 2.54it/s]
56.921693822378856 1
Analysed test_samples from: 88%|███████████████████████████████████████████████▎ | 64/73 [00:22<00:03, 2.55it/s]
56.95518828182627 1
Analysed test_samples from: 89%|████████████████████████████████████████████████ | 65/73 [00:23<00:03, 2.54it/s]
56.48610853898042 1
Analysed test_samples from: 90%|████████████████████████████████████████████████▊ | 66/73 [00:23<00:02, 2.53it/s]
56.24109646413762 1
Analysed test_samples from: 92%|█████████████████████████████████████████████████▌ | 67/73 [00:24<00:02, 2.55it/s]
56.084035832195454 1
Analysed test_samples from: 93%|██████████████████████████████████████████████████▎ | 68/73 [00:24<00:01, 2.54it/s]
55.581455849344394 1
Analysed test_samples from: 95%|███████████████████████████████████████████████████ | 69/73 [00:24<00:01, 2.53it/s]
55.64857859917841 1
Analysed test_samples from: 96%|███████████████████████████████████████████████████▊ | 70/73 [00:25<00:01, 2.56it/s]
55.90829478334101 1
Analysed test_samples from: 97%|████████████████████████████████████████████████████▌ | 71/73 [00:25<00:00, 2.58it/s]
56.825451952760645 1
Analysed test_samples from: 99%|█████████████████████████████████████████████████████▎| 72/73 [00:26<00:00, 2.56it/s]
56.84434568183184 1
Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:26<00:00, 2.75it/s]
58.51117650186915 False False
Moskwa model selection: 4%|██▏ | 1/27 [00:00<00:03, 7.28it/s]
(0, 0, 0) 2 1 Order: (0, 0, 0), AICc: 1706.80, ADF p-value: 0.0199, Best_Order: (0, 0, 0), Best_aicc: 1706.80 (0, 0, 1) 2
Moskwa model selection: 7%|████▎ | 2/27 [00:00<00:04, 5.34it/s]
1 Order: (0, 0, 1), AICc: 1515.47, ADF p-value: 0.0343, Best_Order: (0, 0, 1), Best_aicc: 1515.47 (0, 0, 2) 2
Moskwa model selection: 15%|████████▋ | 4/27 [00:00<00:04, 5.34it/s]
1 Order: (0, 0, 2), AICc: 1484.24, ADF p-value: 0.0768, Best_Order: (0, 0, 1), Best_aicc: 1515.47 (0, 1, 0) 2 1 Order: (0, 1, 0), AICc: 1310.84, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 1310.84 (0, 1, 1) 2
Moskwa model selection: 19%|██████████▉ | 5/27 [00:00<00:04, 4.91it/s]
1 Order: (0, 1, 1), AICc: 1295.58, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 1295.58 (0, 1, 2) 2
Moskwa model selection: 26%|███████████████▎ | 7/27 [00:01<00:03, 5.22it/s]
1 Order: (0, 1, 2), AICc: 1267.18, ADF p-value: 0.0023, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (0, 2, 0) 2 1 Order: (0, 2, 0), AICc: 1524.30, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (0, 2, 1) 2
Moskwa model selection: 30%|█████████████████▍ | 8/27 [00:01<00:03, 5.29it/s]
1 Order: (0, 2, 1), AICc: 1309.60, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (0, 2, 2) 2
Moskwa model selection: 33%|███████████████████▋ | 9/27 [00:01<00:03, 4.57it/s]
1 Order: (0, 2, 2), AICc: 1294.81, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (1, 0, 0) 2 1 Order: (1, 0, 0), AICc: 1318.21, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18
Moskwa model selection: 37%|█████████████████████▍ | 10/27 [00:02<00:03, 4.69it/s]
(1, 0, 1) 2
Moskwa model selection: 41%|███████████████████████▋ | 11/27 [00:02<00:03, 4.44it/s]
1 Order: (1, 0, 1), AICc: 1303.80, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (1, 0, 2) 2
Moskwa model selection: 48%|███████████████████████████▉ | 13/27 [00:02<00:02, 4.81it/s]
1 Order: (1, 0, 2), AICc: 1277.42, ADF p-value: 0.0036, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (1, 1, 0) 2 1 Order: (1, 1, 0), AICc: 1306.65, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (1, 1, 1) 2
Moskwa model selection: 52%|██████████████████████████████ | 14/27 [00:02<00:02, 4.57it/s]
1 Order: (1, 1, 1), AICc: 1277.40, ADF p-value: 0.0007, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (1, 1, 2) 2
Moskwa model selection: 59%|██████████████████████████████████▎ | 16/27 [00:03<00:02, 4.97it/s]
1 Order: (1, 1, 2), AICc: 1267.32, ADF p-value: 0.0011, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (1, 2, 0) 2 1 Order: (1, 2, 0), AICc: 1462.77, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (1, 2, 1) 2
Moskwa model selection: 63%|████████████████████████████████████▌ | 17/27 [00:03<00:02, 4.40it/s]
1 Order: (1, 2, 1), AICc: 1305.84, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (1, 2, 2) 2
Moskwa model selection: 67%|██████████████████████████████████████▋ | 18/27 [00:03<00:02, 4.05it/s]
1 Order: (1, 2, 2), AICc: 1303.74, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (2, 0, 0) 2
Moskwa model selection: 70%|████████████████████████████████████████▊ | 19/27 [00:04<00:01, 4.11it/s]
1 Order: (2, 0, 0), AICc: 1311.87, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (2, 0, 1) 2
Moskwa model selection: 74%|██████████████████████████████████████████▉ | 20/27 [00:04<00:01, 3.75it/s]
1 Order: (2, 0, 1), AICc: 1304.40, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (2, 0, 2) 2
Moskwa model selection: 81%|███████████████████████████████████████████████▎ | 22/27 [00:04<00:01, 4.16it/s]
1 Order: (2, 0, 2), AICc: 1278.56, ADF p-value: 0.0022, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (2, 1, 0) 2 1 Order: (2, 1, 0), AICc: 1286.50, ADF p-value: 0.0001, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (2, 1, 1) 2
Moskwa model selection: 85%|█████████████████████████████████████████████████▍ | 23/27 [00:05<00:01, 3.77it/s]
1 Order: (2, 1, 1), AICc: 1277.30, ADF p-value: 0.0004, Best_Order: (0, 1, 2), Best_aicc: 1267.18 (2, 1, 2) 2
Moskwa model selection: 89%|███████████████████████████████████████████████████▌ | 24/27 [00:05<00:00, 3.55it/s]
1 Order: (2, 1, 2), AICc: 1264.30, ADF p-value: 0.0037, Best_Order: (2, 1, 2), Best_aicc: 1264.30 (2, 2, 0) 2 1 Order: (2, 2, 0), AICc: 1390.33, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 1264.30
Moskwa model selection: 93%|█████████████████████████████████████████████████████▋ | 25/27 [00:05<00:00, 3.91it/s]
(2, 2, 1) 2
Moskwa model selection: 96%|███████████████████████████████████████████████████████▊ | 26/27 [00:06<00:00, 3.53it/s]
1 Order: (2, 2, 1), AICc: 1290.55, ADF p-value: 0.0002, Best_Order: (2, 1, 2), Best_aicc: 1264.30 (2, 2, 2) 2
Moskwa model selection: 100%|██████████████████████████████████████████████████████████| 27/27 [00:06<00:00, 4.16it/s]
1 Order: (2, 2, 2), AICc: 1284.15, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 1264.30
Analysed test_samples from: 0%| | 0/29 [00:00<?, ?it/s]
1
Analysed test_samples from: 3%|█▉ | 1/29 [00:00<00:07, 3.51it/s]
71.18240566066339 1
Analysed test_samples from: 7%|███▊ | 2/29 [00:00<00:07, 3.43it/s]
67.87308804432368 1
Analysed test_samples from: 10%|█████▋ | 3/29 [00:00<00:07, 3.39it/s]
72.74953326462177 1
Analysed test_samples from: 14%|███████▌ | 4/29 [00:01<00:07, 3.26it/s]
65.67984875850792 1
Analysed test_samples from: 17%|█████████▍ | 5/29 [00:01<00:07, 3.30it/s]
64.42972463327679 1
Analysed test_samples from: 21%|███████████▍ | 6/29 [00:01<00:08, 2.85it/s]
61.885643418205646 1 62.03212734138276
Analysed test_samples from: 28%|███████████████▏ | 8/29 [00:02<00:05, 3.77it/s]
1 59.33133416779293 1
Analysed test_samples from: 31%|█████████████████ | 9/29 [00:02<00:04, 4.14it/s]
67.89796853476662 1
Analysed test_samples from: 34%|██████████████████▌ | 10/29 [00:02<00:04, 3.83it/s]
66.9392621640329 1
Analysed test_samples from: 38%|████████████████████▍ | 11/29 [00:03<00:04, 3.81it/s]
62.74877282124183 1
Analysed test_samples from: 41%|██████████████████████▎ | 12/29 [00:03<00:04, 3.83it/s]
65.83984872557342 1
Analysed test_samples from: 45%|████████████████████████▏ | 13/29 [00:03<00:04, 3.67it/s]
70.76848351523398 1
Analysed test_samples from: 48%|██████████████████████████ | 14/29 [00:03<00:04, 3.60it/s]
70.48743080762776 1
Analysed test_samples from: 52%|███████████████████████████▉ | 15/29 [00:04<00:04, 3.46it/s]
66.240860397198 1
Analysed test_samples from: 55%|█████████████████████████████▊ | 16/29 [00:04<00:04, 2.95it/s]
57.590116875342176 1
Analysed test_samples from: 62%|█████████████████████████████████▌ | 18/29 [00:05<00:03, 3.15it/s]
53.296666680057086 1 54.1855151157625 1
Analysed test_samples from: 66%|███████████████████████████████████▍ | 19/29 [00:05<00:03, 3.21it/s]
52.75678947228517 1
Analysed test_samples from: 69%|█████████████████████████████████████▏ | 20/29 [00:05<00:02, 3.23it/s]
49.74949739582817 1
Analysed test_samples from: 72%|███████████████████████████████████████ | 21/29 [00:06<00:02, 3.31it/s]
56.829466298528864 1
Analysed test_samples from: 76%|████████████████████████████████████████▉ | 22/29 [00:06<00:02, 2.96it/s]
26.862423767446607 1
Analysed test_samples from: 79%|██████████████████████████████████████████▊ | 23/29 [00:06<00:01, 3.04it/s]
31.195937694899754 1
Analysed test_samples from: 83%|████████████████████████████████████████████▋ | 24/29 [00:07<00:01, 3.17it/s]
31.48386798577277 1
Analysed test_samples from: 86%|██████████████████████████████████████████████▌ | 25/29 [00:07<00:01, 3.36it/s]
45.85482495189922 1
Analysed test_samples from: 90%|████████████████████████████████████████████████▍ | 26/29 [00:07<00:00, 3.05it/s]
38.028273760415296 1
Analysed test_samples from: 93%|██████████████████████████████████████████████████▎ | 27/29 [00:08<00:00, 3.13it/s]
36.74738064284894 1
Analysed test_samples from: 97%|████████████████████████████████████████████████████▏ | 28/29 [00:08<00:00, 3.35it/s]
28.459344895305797 1
Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 29/29 [00:08<00:00, 3.33it/s]
40.276240074417274 False False
Paryż model selection: 0%| | 0/27 [00:00<?, ?it/s]
(0, 0, 0) 2
Paryż model selection: 4%|██▏ | 1/27 [00:00<00:07, 3.25it/s]
1 Order: (0, 0, 0), AICc: 4085.56, ADF p-value: 0.0793, Best_Order: (0, 0, 0), Best_aicc: inf (0, 0, 1) 2
Paryż model selection: 7%|████▍ | 2/27 [00:00<00:06, 3.80it/s]
1 Order: (0, 0, 1), AICc: 3714.64, ADF p-value: 0.0523, Best_Order: (0, 0, 0), Best_aicc: inf (0, 0, 2) 2
Paryż model selection: 11%|██████▋ | 3/27 [00:00<00:06, 3.56it/s]
1 Order: (0, 0, 2), AICc: 3565.38, ADF p-value: 0.0879, Best_Order: (0, 0, 0), Best_aicc: inf (0, 1, 0) 2
Paryż model selection: 15%|████████▉ | 4/27 [00:01<00:05, 3.89it/s]
1 Order: (0, 1, 0), AICc: 3321.62, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3321.62 (0, 1, 1) 2 1 Order: (0, 1, 1), AICc: 3310.77, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3310.77
Paryż model selection: 19%|███████████ | 5/27 [00:01<00:04, 4.48it/s]
(0, 1, 2) 2 1
Paryż model selection: 22%|█████████████▎ | 6/27 [00:01<00:04, 4.52it/s]
Order: (0, 1, 2), AICc: 3255.19, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19 (0, 2, 0) 2
Paryż model selection: 26%|███████████████▌ | 7/27 [00:01<00:04, 4.45it/s]
1 Order: (0, 2, 0), AICc: 3810.11, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19 (0, 2, 1) 2
Paryż model selection: 30%|█████████████████▊ | 8/27 [00:02<00:05, 3.50it/s]
1 Order: (0, 2, 1), AICc: 3321.69, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19 (0, 2, 2) 2
Paryż model selection: 33%|████████████████████ | 9/27 [00:02<00:06, 2.71it/s]
1 Order: (0, 2, 2), AICc: 3310.21, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19 (1, 0, 0) 2
Paryż model selection: 37%|█████████████████████▊ | 10/27 [00:02<00:05, 2.93it/s]
1 Order: (1, 0, 0), AICc: 3327.17, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19 (1, 0, 1) 2
Paryż model selection: 41%|████████████████████████ | 11/27 [00:03<00:06, 2.61it/s]
1 Order: (1, 0, 1), AICc: 3315.27, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19 (1, 0, 2) 2
Paryż model selection: 48%|████████████████████████████▍ | 13/27 [00:04<00:04, 2.85it/s]
1 Order: (1, 0, 2), AICc: 3278.54, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19 (1, 1, 0) 2 1 Order: (1, 1, 0), AICc: 3319.76, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19 (1, 1, 1) 2
Paryż model selection: 52%|██████████████████████████████▌ | 14/27 [00:04<00:04, 2.99it/s]
1 Order: (1, 1, 1), AICc: 3255.50, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19 (1, 1, 2) 2
Paryż model selection: 56%|████████████████████████████████▊ | 15/27 [00:04<00:04, 2.93it/s]
1 Order: (1, 1, 2), AICc: 3243.38, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38 (1, 2, 0) 2 1
Paryż model selection: 59%|██████████████████████████████████▉ | 16/27 [00:04<00:03, 3.34it/s]
Order: (1, 2, 0), AICc: 3691.84, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38 (1, 2, 1) 2
Paryż model selection: 63%|█████████████████████████████████████▏ | 17/27 [00:05<00:03, 3.05it/s]
1 Order: (1, 2, 1), AICc: 3319.52, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38 (1, 2, 2) 2
Paryż model selection: 67%|███████████████████████████████████████▎ | 18/27 [00:05<00:03, 2.41it/s]
1 Order: (1, 2, 2), AICc: 3320.39, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38 (2, 0, 0) 2
Paryż model selection: 70%|█████████████████████████████████████████▌ | 19/27 [00:06<00:03, 2.40it/s]
1 Order: (2, 0, 0), AICc: 3319.81, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38 (2, 0, 1) 2
Paryż model selection: 74%|███████████████████████████████████████████▋ | 20/27 [00:07<00:03, 2.04it/s]
1 Order: (2, 0, 1), AICc: 3350.31, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38 (2, 0, 2) 2
Paryż model selection: 78%|█████████████████████████████████████████████▉ | 21/27 [00:07<00:03, 1.87it/s]
1 Order: (2, 0, 2), AICc: 3294.69, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38 (2, 1, 0) 2
Paryż model selection: 81%|████████████████████████████████████████████████ | 22/27 [00:07<00:02, 2.27it/s]
1 Order: (2, 1, 0), AICc: 3275.46, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38 (2, 1, 1) 2
Paryż model selection: 85%|██████████████████████████████████████████████████▎ | 23/27 [00:08<00:01, 2.39it/s]
1 Order: (2, 1, 1), AICc: 3250.18, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38 (2, 1, 2) 2
Paryż model selection: 89%|████████████████████████████████████████████████████▍ | 24/27 [00:08<00:01, 2.31it/s]
1 Order: (2, 1, 2), AICc: 3244.84, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38 (2, 2, 0) 2
Paryż model selection: 93%|██████████████████████████████████████████████████████▋ | 25/27 [00:08<00:00, 2.70it/s]
1 Order: (2, 2, 0), AICc: 3570.80, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38 (2, 2, 1) 2
Paryż model selection: 96%|████████████████████████████████████████████████████████▊ | 26/27 [00:09<00:00, 2.40it/s]
1 Order: (2, 2, 1), AICc: 3280.02, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38 (2, 2, 2) 2
Paryż model selection: 100%|███████████████████████████████████████████████████████████| 27/27 [00:10<00:00, 2.63it/s]
1 Order: (2, 2, 2), AICc: 3253.56, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
Analysed test_samples from: 0%| | 0/73 [00:00<?, ?it/s]
1
Analysed test_samples from: 1%|▊ | 1/73 [00:00<00:23, 3.02it/s]
61.47314607721131 1
Analysed test_samples from: 3%|█▌ | 2/73 [00:00<00:22, 3.20it/s]
59.37122706610978 1
Analysed test_samples from: 4%|██▎ | 3/73 [00:00<00:21, 3.26it/s]
59.57556375891001 1
Analysed test_samples from: 5%|███ | 4/73 [00:01<00:20, 3.43it/s]
57.85456104979216 1
Analysed test_samples from: 7%|███▊ | 5/73 [00:01<00:19, 3.45it/s]
58.57114000977831 1
Analysed test_samples from: 8%|████▌ | 6/73 [00:01<00:19, 3.43it/s]
59.68496235390461 1
Analysed test_samples from: 10%|█████▎ | 7/73 [00:02<00:18, 3.50it/s]
58.11385099625953 1
Analysed test_samples from: 11%|██████ | 8/73 [00:02<00:18, 3.47it/s]
59.54910711588671 1
Analysed test_samples from: 12%|██████▊ | 9/73 [00:02<00:18, 3.55it/s]
60.05973587768145 1
Analysed test_samples from: 14%|███████▍ | 10/73 [00:02<00:17, 3.55it/s]
59.65100903118713 1
Analysed test_samples from: 15%|████████▏ | 11/73 [00:03<00:17, 3.56it/s]
58.34824261453546 1
Analysed test_samples from: 16%|████████▉ | 12/73 [00:03<00:17, 3.53it/s]
59.71294921412107 1
Analysed test_samples from: 18%|█████████▌ | 13/73 [00:03<00:16, 3.54it/s]
60.416479070953116 1
Analysed test_samples from: 19%|██████████▎ | 14/73 [00:04<00:17, 3.46it/s]
56.39383192904149 1
Analysed test_samples from: 21%|███████████ | 15/73 [00:04<00:16, 3.46it/s]
56.35777653408947 1
Analysed test_samples from: 22%|███████████▊ | 16/73 [00:04<00:16, 3.53it/s]
56.752340912013025 1
Analysed test_samples from: 23%|████████████▌ | 17/73 [00:04<00:15, 3.52it/s]
57.47056855412603 1
Analysed test_samples from: 25%|█████████████▎ | 18/73 [00:05<00:15, 3.53it/s]
58.12084236330804 1
Analysed test_samples from: 26%|██████████████ | 19/73 [00:05<00:15, 3.55it/s]
57.10130136484926 1
Analysed test_samples from: 27%|██████████████▊ | 20/73 [00:05<00:15, 3.50it/s]
58.047119136905636 1
Analysed test_samples from: 29%|███████████████▌ | 21/73 [00:06<00:14, 3.47it/s]
57.34921825095911 1
Analysed test_samples from: 30%|████████████████▎ | 22/73 [00:06<00:14, 3.46it/s]
56.20151514248222 1
Analysed test_samples from: 32%|█████████████████ | 23/73 [00:06<00:14, 3.49it/s]
55.77539227366583 1
Analysed test_samples from: 33%|█████████████████▊ | 24/73 [00:06<00:14, 3.45it/s]
54.975831260389796 1
Analysed test_samples from: 34%|██████████████████▍ | 25/73 [00:07<00:13, 3.44it/s]
61.05578959231484 1
Analysed test_samples from: 36%|███████████████████▏ | 26/73 [00:07<00:13, 3.42it/s]
59.6442659348958 1
Analysed test_samples from: 37%|███████████████████▉ | 27/73 [00:07<00:13, 3.35it/s]
58.25580805988456 1
Analysed test_samples from: 38%|████████████████████▋ | 28/73 [00:08<00:13, 3.40it/s]
54.64395934124299 1
Analysed test_samples from: 40%|█████████████████████▍ | 29/73 [00:08<00:12, 3.44it/s]
56.27822350032338 1
Analysed test_samples from: 41%|██████████████████████▏ | 30/73 [00:08<00:12, 3.48it/s]
58.09728033560655 1
Analysed test_samples from: 42%|██████████████████████▉ | 31/73 [00:08<00:11, 3.51it/s]
59.6086100437044 1
Analysed test_samples from: 44%|███████████████████████▋ | 32/73 [00:09<00:11, 3.58it/s]
57.842454922042315 1
Analysed test_samples from: 45%|████████████████████████▍ | 33/73 [00:09<00:11, 3.57it/s]
58.398655792368345 1
Analysed test_samples from: 47%|█████████████████████████▏ | 34/73 [00:09<00:10, 3.59it/s]
53.3027844156787 1
Analysed test_samples from: 48%|█████████████████████████▉ | 35/73 [00:10<00:10, 3.47it/s]
57.857356070503016 1
Analysed test_samples from: 49%|██████████████████████████▋ | 36/73 [00:10<00:10, 3.37it/s]
55.152163644475635 1
Analysed test_samples from: 51%|███████████████████████████▎ | 37/73 [00:10<00:10, 3.31it/s]
51.653922997909085 1
Analysed test_samples from: 52%|████████████████████████████ | 38/73 [00:11<00:10, 3.31it/s]
51.70665326238125 1
Analysed test_samples from: 53%|████████████████████████████▊ | 39/73 [00:11<00:10, 3.29it/s]
52.82897906760032 1
Analysed test_samples from: 55%|█████████████████████████████▌ | 40/73 [00:11<00:09, 3.32it/s]
52.305460120212345 1
Analysed test_samples from: 56%|██████████████████████████████▎ | 41/73 [00:11<00:09, 3.35it/s]
52.70076108574168 1
Analysed test_samples from: 58%|███████████████████████████████ | 42/73 [00:12<00:09, 3.34it/s]
51.6020494370417 1
Analysed test_samples from: 59%|███████████████████████████████▊ | 43/73 [00:12<00:09, 3.32it/s]
46.4320522975593 1
Analysed test_samples from: 60%|████████████████████████████████▌ | 44/73 [00:12<00:08, 3.33it/s]
44.3606659600579 1
Analysed test_samples from: 62%|█████████████████████████████████▎ | 45/73 [00:13<00:08, 3.30it/s]
45.838866343539166 1
Analysed test_samples from: 63%|██████████████████████████████████ | 46/73 [00:13<00:08, 3.37it/s]
52.70191677284376 1
Analysed test_samples from: 64%|██████████████████████████████████▊ | 47/73 [00:13<00:07, 3.37it/s]
54.69473670886483 1
Analysed test_samples from: 66%|███████████████████████████████████▌ | 48/73 [00:14<00:07, 3.29it/s]
52.97670794991379 1
Analysed test_samples from: 67%|████████████████████████████████████▏ | 49/73 [00:14<00:07, 3.26it/s]
50.93725168282195 1
Analysed test_samples from: 68%|████████████████████████████████████▉ | 50/73 [00:14<00:07, 3.24it/s]
54.25857432858504 1
Analysed test_samples from: 70%|█████████████████████████████████████▋ | 51/73 [00:14<00:06, 3.24it/s]
54.60237244814358 1
Analysed test_samples from: 71%|██████████████████████████████████████▍ | 52/73 [00:15<00:06, 3.31it/s]
56.703497906110094 1
Analysed test_samples from: 73%|███████████████████████████████████████▏ | 53/73 [00:15<00:05, 3.34it/s]
58.202913098882775 1
Analysed test_samples from: 74%|███████████████████████████████████████▉ | 54/73 [00:15<00:05, 3.35it/s]
58.85553404630832 1
Analysed test_samples from: 75%|████████████████████████████████████████▋ | 55/73 [00:16<00:05, 3.38it/s]
56.79942868446271 1
Analysed test_samples from: 77%|█████████████████████████████████████████▍ | 56/73 [00:16<00:05, 3.34it/s]
55.711252378268085 1
Analysed test_samples from: 78%|██████████████████████████████████████████▏ | 57/73 [00:16<00:04, 3.33it/s]
55.55257388178002 1
Analysed test_samples from: 79%|██████████████████████████████████████████▉ | 58/73 [00:17<00:04, 3.33it/s]
53.2984253437562 1
Analysed test_samples from: 81%|███████████████████████████████████████████▋ | 59/73 [00:17<00:04, 3.32it/s]
46.67938026896452 1
Analysed test_samples from: 82%|████████████████████████████████████████████▍ | 60/73 [00:17<00:03, 3.36it/s]
47.53054149655809 1
Analysed test_samples from: 84%|█████████████████████████████████████████████ | 61/73 [00:17<00:03, 3.32it/s]
53.10397305904415 1
Analysed test_samples from: 85%|█████████████████████████████████████████████▊ | 62/73 [00:18<00:03, 3.15it/s]
56.58415728157573 1
Analysed test_samples from: 86%|██████████████████████████████████████████████▌ | 63/73 [00:18<00:03, 3.11it/s]
57.48088811837981 1
Analysed test_samples from: 88%|███████████████████████████████████████████████▎ | 64/73 [00:18<00:03, 2.97it/s]
57.586187849726336 1
Analysed test_samples from: 89%|████████████████████████████████████████████████ | 65/73 [00:19<00:02, 3.07it/s]
57.95494585964413 1
Analysed test_samples from: 90%|████████████████████████████████████████████████▊ | 66/73 [00:19<00:02, 2.93it/s]
58.102754257703396 1
Analysed test_samples from: 92%|█████████████████████████████████████████████████▌ | 67/73 [00:20<00:02, 2.89it/s]
59.07990652307736 1
Analysed test_samples from: 93%|██████████████████████████████████████████████████▎ | 68/73 [00:20<00:01, 2.97it/s]
57.473061219356126 1
Analysed test_samples from: 95%|███████████████████████████████████████████████████ | 69/73 [00:20<00:01, 2.97it/s]
55.78413566742749 1
Analysed test_samples from: 96%|███████████████████████████████████████████████████▊ | 70/73 [00:20<00:00, 3.08it/s]
58.78916213926437 1
Analysed test_samples from: 97%|████████████████████████████████████████████████████▌ | 71/73 [00:21<00:00, 3.14it/s]
58.698240967903956 1
Analysed test_samples from: 99%|█████████████████████████████████████████████████████▎| 72/73 [00:21<00:00, 3.18it/s]
57.945053286398156 1
Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:21<00:00, 3.33it/s]
55.728440492325696 False False
Praga model selection: 0%| | 0/27 [00:00<?, ?it/s]
(0, 0, 0) 2
Praga model selection: 4%|██▏ | 1/27 [00:00<00:06, 4.05it/s]
1 Order: (0, 0, 0), AICc: 4023.48, ADF p-value: 0.0057, Best_Order: (0, 0, 0), Best_aicc: 4023.48 (0, 0, 1) 2
Praga model selection: 7%|████▍ | 2/27 [00:00<00:06, 3.66it/s]
1 Order: (0, 0, 1), AICc: 3730.51, ADF p-value: 0.0458, Best_Order: (0, 0, 1), Best_aicc: 3730.51 (0, 0, 2) 2
Praga model selection: 11%|██████▋ | 3/27 [00:00<00:07, 3.32it/s]
1 Order: (0, 0, 2), AICc: 3651.67, ADF p-value: 0.0037, Best_Order: (0, 0, 2), Best_aicc: 3651.67 (0, 1, 0) 2
Praga model selection: 15%|████████▉ | 4/27 [00:01<00:06, 3.58it/s]
1 Order: (0, 1, 0), AICc: 3432.74, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3432.74 (0, 1, 1) 2 1 Order: (0, 1, 1), AICc: 3393.72, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3393.72
Praga model selection: 19%|███████████ | 5/27 [00:01<00:05, 4.18it/s]
(0, 1, 2) 2
Praga model selection: 22%|█████████████▎ | 6/27 [00:01<00:05, 4.00it/s]
1 Order: (0, 1, 2), AICc: 3352.52, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52 (0, 2, 0) 2
Praga model selection: 26%|███████████████▌ | 7/27 [00:01<00:04, 4.22it/s]
1 Order: (0, 2, 0), AICc: 3947.67, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52 (0, 2, 1) 2
Praga model selection: 30%|█████████████████▊ | 8/27 [00:02<00:05, 3.32it/s]
1 Order: (0, 2, 1), AICc: 3431.92, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52 (0, 2, 2) 2
Praga model selection: 33%|████████████████████ | 9/27 [00:02<00:07, 2.55it/s]
1 Order: (0, 2, 2), AICc: 3393.26, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52 (1, 0, 0) 2
Praga model selection: 37%|█████████████████████▊ | 10/27 [00:03<00:06, 2.76it/s]
1 Order: (1, 0, 0), AICc: 3436.75, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52 (1, 0, 1) 2
Praga model selection: 41%|████████████████████████ | 11/27 [00:03<00:06, 2.50it/s]
1 Order: (1, 0, 1), AICc: 3399.29, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52 (1, 0, 2) 2
Praga model selection: 48%|████████████████████████████▍ | 13/27 [00:04<00:05, 2.80it/s]
1 Order: (1, 0, 2), AICc: 3358.76, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52 (1, 1, 0) 2 1 Order: (1, 1, 0), AICc: 3418.12, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52 (1, 1, 1) 2
Praga model selection: 52%|██████████████████████████████▌ | 14/27 [00:04<00:04, 2.91it/s]
1 Order: (1, 1, 1), AICc: 3350.67, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 3350.67 (1, 1, 2) 2
Praga model selection: 56%|████████████████████████████████▊ | 15/27 [00:04<00:04, 2.88it/s]
1 Order: (1, 1, 2), AICc: 3347.47, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47 (1, 2, 0) 2 1
Praga model selection: 59%|██████████████████████████████████▉ | 16/27 [00:05<00:03, 3.30it/s]
Order: (1, 2, 0), AICc: 3801.00, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47 (1, 2, 1) 2
Praga model selection: 63%|█████████████████████████████████████▏ | 17/27 [00:05<00:03, 2.74it/s]
1 Order: (1, 2, 1), AICc: 3417.41, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47 (1, 2, 2) 2
Praga model selection: 67%|███████████████████████████████████████▎ | 18/27 [00:06<00:04, 2.17it/s]
1 Order: (1, 2, 2), AICc: 3352.15, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47 (2, 0, 0) 2
Praga model selection: 70%|█████████████████████████████████████████▌ | 19/27 [00:06<00:03, 2.15it/s]
1 Order: (2, 0, 0), AICc: 3418.03, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47 (2, 0, 1) 2
Praga model selection: 74%|███████████████████████████████████████████▋ | 20/27 [00:07<00:03, 2.07it/s]
1 Order: (2, 0, 1), AICc: 3365.36, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47 (2, 0, 2) 2
Praga model selection: 78%|█████████████████████████████████████████████▉ | 21/27 [00:07<00:03, 1.89it/s]
1 Order: (2, 0, 2), AICc: 3358.93, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47 (2, 1, 0) 2
Praga model selection: 81%|████████████████████████████████████████████████ | 22/27 [00:08<00:02, 2.24it/s]
1 Order: (2, 1, 0), AICc: 3382.31, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47 (2, 1, 1) 2
Praga model selection: 85%|██████████████████████████████████████████████████▎ | 23/27 [00:08<00:01, 2.40it/s]
1 Order: (2, 1, 1), AICc: 3352.53, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47 (2, 1, 2) 2
Praga model selection: 89%|████████████████████████████████████████████████████▍ | 24/27 [00:09<00:01, 2.27it/s]
1 Order: (2, 1, 2), AICc: 3348.48, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47 (2, 2, 0) 2
Praga model selection: 93%|██████████████████████████████████████████████████████▋ | 25/27 [00:09<00:00, 2.63it/s]
1 Order: (2, 2, 0), AICc: 3676.88, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47 (2, 2, 1) 2
Praga model selection: 96%|████████████████████████████████████████████████████████▊ | 26/27 [00:09<00:00, 2.21it/s]
1 Order: (2, 2, 1), AICc: 3386.71, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47 (2, 2, 2) 2
Praga model selection: 100%|███████████████████████████████████████████████████████████| 27/27 [00:10<00:00, 2.53it/s]
1 Order: (2, 2, 2), AICc: 3355.86, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
Analysed test_samples from: 0%| | 0/73 [00:00<?, ?it/s]
1
Analysed test_samples from: 1%|▊ | 1/73 [00:00<00:24, 2.97it/s]
49.00555036304587 1
Analysed test_samples from: 3%|█▌ | 2/73 [00:00<00:25, 2.84it/s]
52.52454547440489 1
Analysed test_samples from: 4%|██▎ | 3/73 [00:01<00:25, 2.79it/s]
51.116360452495854 1
Analysed test_samples from: 5%|███ | 4/73 [00:01<00:24, 2.87it/s]
50.39444379929054 1
Analysed test_samples from: 7%|███▊ | 5/73 [00:01<00:23, 2.89it/s]
52.011898941346075 1
Analysed test_samples from: 8%|████▌ | 6/73 [00:02<00:22, 3.04it/s]
50.9479316354393 1
Analysed test_samples from: 10%|█████▎ | 7/73 [00:02<00:22, 2.93it/s]
52.01327658978862 1
Analysed test_samples from: 11%|██████ | 8/73 [00:02<00:21, 3.05it/s]
51.29950721011723 1
Analysed test_samples from: 12%|██████▊ | 9/73 [00:03<00:21, 2.99it/s]
50.94257557289532 1
Analysed test_samples from: 14%|███████▍ | 10/73 [00:03<00:21, 2.87it/s]
50.9537679346203 1
Analysed test_samples from: 15%|████████▏ | 11/73 [00:03<00:21, 2.93it/s]
52.59643095821044 1
Analysed test_samples from: 16%|████████▉ | 12/73 [00:04<00:21, 2.88it/s]
51.79088694145406 1
Analysed test_samples from: 18%|█████████▌ | 13/73 [00:04<00:20, 2.89it/s]
51.296582296081546 1
Analysed test_samples from: 19%|██████████▎ | 14/73 [00:04<00:20, 2.95it/s]
50.18030426814833 1
Analysed test_samples from: 21%|███████████ | 15/73 [00:05<00:20, 2.88it/s]
48.711315564525876 1
Analysed test_samples from: 22%|███████████▊ | 16/73 [00:05<00:19, 2.96it/s]
46.39220620748705 1
Analysed test_samples from: 23%|████████████▌ | 17/73 [00:05<00:18, 3.01it/s]
48.938263130194514 1
Analysed test_samples from: 25%|█████████████▎ | 18/73 [00:06<00:18, 2.92it/s]
49.985445309421976 1
Analysed test_samples from: 26%|██████████████ | 19/73 [00:06<00:18, 2.92it/s]
48.421004515848956 1
Analysed test_samples from: 27%|██████████████▊ | 20/73 [00:06<00:18, 2.89it/s]
46.73448317849832 1
Analysed test_samples from: 29%|███████████████▌ | 21/73 [00:07<00:17, 2.91it/s]
45.04607800460027 1
Analysed test_samples from: 30%|████████████████▎ | 22/73 [00:07<00:18, 2.74it/s]
46.60987998978342 1
Analysed test_samples from: 32%|█████████████████ | 23/73 [00:07<00:18, 2.73it/s]
45.49543456970162 1
Analysed test_samples from: 33%|█████████████████▊ | 24/73 [00:08<00:18, 2.64it/s]
44.35072970576405 1
Analysed test_samples from: 34%|██████████████████▍ | 25/73 [00:08<00:17, 2.74it/s]
47.001999189736104 1
Analysed test_samples from: 36%|███████████████████▏ | 26/73 [00:09<00:16, 2.83it/s]
49.08302229837311 1
Analysed test_samples from: 37%|███████████████████▉ | 27/73 [00:09<00:16, 2.86it/s]
49.12173282550087 1
Analysed test_samples from: 38%|████████████████████▋ | 28/73 [00:09<00:15, 2.89it/s]
47.54751729945408 1
Analysed test_samples from: 40%|█████████████████████▍ | 29/73 [00:10<00:15, 2.88it/s]
43.13056706472473 1
Analysed test_samples from: 41%|██████████████████████▏ | 30/73 [00:10<00:13, 3.08it/s]
43.0427571020094 1
Analysed test_samples from: 42%|██████████████████████▉ | 31/73 [00:10<00:13, 3.16it/s]
47.318862029358755 1
Analysed test_samples from: 44%|███████████████████████▋ | 32/73 [00:10<00:13, 3.12it/s]
49.60877376001068 1
Analysed test_samples from: 45%|████████████████████████▍ | 33/73 [00:11<00:13, 3.03it/s]
47.850280074050964 1
Analysed test_samples from: 47%|█████████████████████████▏ | 34/73 [00:11<00:13, 2.96it/s]
41.04560859539383 1
Analysed test_samples from: 48%|█████████████████████████▉ | 35/73 [00:12<00:12, 2.94it/s]
43.04701706242342 1
Analysed test_samples from: 49%|██████████████████████████▋ | 36/73 [00:12<00:12, 2.88it/s]
43.921826107788505 1
Analysed test_samples from: 51%|███████████████████████████▎ | 37/73 [00:12<00:12, 2.89it/s]
40.54887316406719 1
Analysed test_samples from: 52%|████████████████████████████ | 38/73 [00:13<00:11, 2.94it/s]
39.21892023115761 1
Analysed test_samples from: 53%|████████████████████████████▊ | 39/73 [00:13<00:12, 2.73it/s]
42.44728891510631 1
Analysed test_samples from: 55%|█████████████████████████████▌ | 40/73 [00:13<00:11, 2.78it/s]
37.80330749020048 1
Analysed test_samples from: 56%|██████████████████████████████▎ | 41/73 [00:14<00:11, 2.68it/s]
37.673814397289014 1
Analysed test_samples from: 58%|███████████████████████████████ | 42/73 [00:14<00:11, 2.63it/s]
40.224447884405414 1
Analysed test_samples from: 59%|███████████████████████████████▊ | 43/73 [00:14<00:10, 2.73it/s]
35.004642083391545 1
Analysed test_samples from: 60%|████████████████████████████████▌ | 44/73 [00:15<00:10, 2.68it/s]
33.7425305289381 1
Analysed test_samples from: 62%|█████████████████████████████████▎ | 45/73 [00:15<00:10, 2.62it/s]
36.27563975884935 1
Analysed test_samples from: 63%|██████████████████████████████████ | 46/73 [00:16<00:10, 2.56it/s]
33.50804133793367 1
Analysed test_samples from: 64%|██████████████████████████████████▊ | 47/73 [00:16<00:09, 2.64it/s]
35.52939048154697 1
Analysed test_samples from: 66%|███████████████████████████████████▌ | 48/73 [00:16<00:09, 2.76it/s]
38.2120199018916 1
Analysed test_samples from: 67%|████████████████████████████████████▏ | 49/73 [00:17<00:08, 2.78it/s]
41.97017613074813 1
Analysed test_samples from: 68%|████████████████████████████████████▉ | 50/73 [00:17<00:08, 2.82it/s]
37.50150646974099 1
Analysed test_samples from: 70%|█████████████████████████████████████▋ | 51/73 [00:17<00:07, 2.76it/s]
40.41917334823863 1
Analysed test_samples from: 71%|██████████████████████████████████████▍ | 52/73 [00:18<00:07, 2.71it/s]
44.01771994793713 1
Analysed test_samples from: 73%|███████████████████████████████████████▏ | 53/73 [00:18<00:07, 2.62it/s]
47.072557108225666 1
Analysed test_samples from: 74%|███████████████████████████████████████▉ | 54/73 [00:19<00:07, 2.70it/s]
46.95645846437812 1
Analysed test_samples from: 75%|████████████████████████████████████████▋ | 55/73 [00:19<00:06, 2.62it/s]
47.695247759517464 1
Analysed test_samples from: 77%|█████████████████████████████████████████▍ | 56/73 [00:19<00:06, 2.59it/s]
45.25348025342529 1
Analysed test_samples from: 78%|██████████████████████████████████████████▏ | 57/73 [00:20<00:06, 2.64it/s]
43.65013605722753 1
Analysed test_samples from: 79%|██████████████████████████████████████████▉ | 58/73 [00:20<00:05, 2.62it/s]
44.46662322285012 1
Analysed test_samples from: 81%|███████████████████████████████████████████▋ | 59/73 [00:20<00:05, 2.61it/s]
44.5847558263051 1
Analysed test_samples from: 82%|████████████████████████████████████████████▍ | 60/73 [00:21<00:04, 2.61it/s]
43.13492155585726 1
Analysed test_samples from: 84%|█████████████████████████████████████████████ | 61/73 [00:21<00:04, 2.63it/s]
43.61555497966765 1
Analysed test_samples from: 85%|█████████████████████████████████████████████▊ | 62/73 [00:22<00:04, 2.60it/s]
44.560515947567254 1
Analysed test_samples from: 86%|██████████████████████████████████████████████▌ | 63/73 [00:22<00:03, 2.58it/s]
46.03256333766684 1
Analysed test_samples from: 88%|███████████████████████████████████████████████▎ | 64/73 [00:22<00:03, 2.58it/s]
41.276133876050224 1
Analysed test_samples from: 89%|████████████████████████████████████████████████ | 65/73 [00:23<00:03, 2.57it/s]
40.524435837122624 1
Analysed test_samples from: 90%|████████████████████████████████████████████████▊ | 66/73 [00:23<00:02, 2.63it/s]
47.11890230927597 1
Analysed test_samples from: 92%|█████████████████████████████████████████████████▌ | 67/73 [00:24<00:02, 2.71it/s]
49.94217762377262 1
Analysed test_samples from: 93%|██████████████████████████████████████████████████▎ | 68/73 [00:24<00:01, 2.61it/s]
48.64617789634856 1
Analysed test_samples from: 95%|███████████████████████████████████████████████████ | 69/73 [00:24<00:01, 2.68it/s]
45.16668060188293 1
Analysed test_samples from: 96%|███████████████████████████████████████████████████▊ | 70/73 [00:25<00:01, 2.71it/s]
45.14223629504769 1
Analysed test_samples from: 97%|████████████████████████████████████████████████████▌ | 71/73 [00:25<00:00, 2.71it/s]
48.17437791513955 1
Analysed test_samples from: 99%|█████████████████████████████████████████████████████▎| 72/73 [00:25<00:00, 2.71it/s]
47.28770984771129 1
Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:26<00:00, 2.78it/s]
44.379693774275566 False False
Warszawa model selection: 0%| | 0/27 [00:00<?, ?it/s]
(0, 0, 0) 2
Warszawa model selection: 7%|████▏ | 2/27 [00:00<00:04, 5.15it/s]
1 Order: (0, 0, 0), AICc: 4132.61, ADF p-value: 0.0001, Best_Order: (0, 0, 0), Best_aicc: 4132.61 (0, 0, 1) 2 1 Order: (0, 0, 1), AICc: 3835.36, ADF p-value: 0.0006, Best_Order: (0, 0, 1), Best_aicc: 3835.36 (0, 0, 2) 2
Warszawa model selection: 11%|██████▎ | 3/27 [00:00<00:05, 4.01it/s]
1 Order: (0, 0, 2), AICc: 3746.31, ADF p-value: 0.0005, Best_Order: (0, 0, 2), Best_aicc: 3746.31 (0, 1, 0) 2
Warszawa model selection: 15%|████████▍ | 4/27 [00:00<00:05, 4.12it/s]
1 Order: (0, 1, 0), AICc: 3517.48, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3517.48 (0, 1, 1) 2
Warszawa model selection: 19%|██████████▌ | 5/27 [00:01<00:05, 3.92it/s]
1 Order: (0, 1, 1), AICc: 3477.49, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3477.49 (0, 1, 2) 2
Warszawa model selection: 22%|████████████▋ | 6/27 [00:01<00:06, 3.49it/s]
1 Order: (0, 1, 2), AICc: 3432.18, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18 (0, 2, 0) 2
Warszawa model selection: 26%|██████████████▊ | 7/27 [00:01<00:05, 3.59it/s]
1 Order: (0, 2, 0), AICc: 4066.43, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18 (0, 2, 1) 2
Warszawa model selection: 30%|████████████████▉ | 8/27 [00:02<00:06, 2.92it/s]
1 Order: (0, 2, 1), AICc: 3517.90, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18 (0, 2, 2) 2
Warszawa model selection: 33%|███████████████████ | 9/27 [00:02<00:07, 2.47it/s]
1 Order: (0, 2, 2), AICc: 3476.74, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18 (1, 0, 0) 2
Warszawa model selection: 37%|████████████████████▋ | 10/27 [00:03<00:06, 2.70it/s]
1 Order: (1, 0, 0), AICc: 3524.76, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18 (1, 0, 1) 2
Warszawa model selection: 41%|██████████████████████▊ | 11/27 [00:03<00:06, 2.51it/s]
1 Order: (1, 0, 1), AICc: 3483.21, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18 (1, 0, 2) 2
Warszawa model selection: 48%|██████████████████████████▉ | 13/27 [00:04<00:05, 2.74it/s]
1 Order: (1, 0, 2), AICc: 3438.62, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18 (1, 1, 0) 2 1 Order: (1, 1, 0), AICc: 3498.55, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18 (1, 1, 1) 2
Warszawa model selection: 52%|█████████████████████████████ | 14/27 [00:04<00:04, 2.76it/s]
1 Order: (1, 1, 1), AICc: 3427.90, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 3427.90 (1, 1, 2) 2
Warszawa model selection: 56%|███████████████████████████████ | 15/27 [00:05<00:04, 2.63it/s]
1 Order: (1, 1, 2), AICc: 3424.72, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72 (1, 2, 0) 2 1
Warszawa model selection: 59%|█████████████████████████████████▏ | 16/27 [00:05<00:03, 3.08it/s]
Order: (1, 2, 0), AICc: 3880.75, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72 (1, 2, 1) 2
Warszawa model selection: 63%|███████████████████████████████████▎ | 17/27 [00:05<00:03, 2.60it/s]
1 Order: (1, 2, 1), AICc: 3497.23, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72 (1, 2, 2) 2
Warszawa model selection: 67%|█████████████████████████████████████▎ | 18/27 [00:06<00:04, 2.03it/s]
1 Order: (1, 2, 2), AICc: 3471.32, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72 (2, 0, 0) 2
Warszawa model selection: 70%|███████████████████████████████████████▍ | 19/27 [00:07<00:03, 2.07it/s]
1 Order: (2, 0, 0), AICc: 3499.07, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72 (2, 0, 1) 2
Warszawa model selection: 74%|█████████████████████████████████████████▍ | 20/27 [00:07<00:03, 2.01it/s]
1 Order: (2, 0, 1), AICc: 3435.06, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72 (2, 0, 2) 2
Warszawa model selection: 78%|███████████████████████████████████████████▌ | 21/27 [00:08<00:03, 1.88it/s]
1 Order: (2, 0, 2), AICc: 3442.28, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72 (2, 1, 0) 2
Warszawa model selection: 81%|█████████████████████████████████████████████▋ | 22/27 [00:08<00:02, 2.26it/s]
1 Order: (2, 1, 0), AICc: 3470.00, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72 (2, 1, 1) 2
Warszawa model selection: 85%|███████████████████████████████████████████████▋ | 23/27 [00:08<00:01, 2.28it/s]
1 Order: (2, 1, 1), AICc: 3429.72, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72 (2, 1, 2) 2
Warszawa model selection: 89%|█████████████████████████████████████████████████▊ | 24/27 [00:09<00:01, 2.13it/s]
1 Order: (2, 1, 2), AICc: 3424.27, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3424.27 (2, 2, 0) 2
Warszawa model selection: 93%|███████████████████████████████████████████████████▊ | 25/27 [00:09<00:00, 2.51it/s]
1 Order: (2, 2, 0), AICc: 3749.22, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3424.27 (2, 2, 1) 2
Warszawa model selection: 96%|█████████████████████████████████████████████████████▉ | 26/27 [00:10<00:00, 2.19it/s]
1 Order: (2, 2, 1), AICc: 3474.27, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3424.27 (2, 2, 2) 2
Warszawa model selection: 100%|████████████████████████████████████████████████████████| 27/27 [00:11<00:00, 2.44it/s]
1 Order: (2, 2, 2), AICc: 3443.71, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3424.27
Analysed test_samples from: 0%| | 0/73 [00:00<?, ?it/s]
1
Analysed test_samples from: 1%|▊ | 1/73 [00:00<00:35, 2.04it/s]
46.821069225850565 1
Analysed test_samples from: 3%|█▌ | 2/73 [00:01<00:40, 1.76it/s]
50.687773161715086 1
Analysed test_samples from: 4%|██▎ | 3/73 [00:01<00:35, 1.97it/s]
52.184781833692234 1
Analysed test_samples from: 5%|███ | 4/73 [00:02<00:36, 1.88it/s]
52.33187437636305 1
Analysed test_samples from: 7%|███▊ | 5/73 [00:02<00:34, 1.95it/s]
50.22031714864876 1
Analysed test_samples from: 8%|████▌ | 6/73 [00:03<00:36, 1.82it/s]
51.47298971337308 1
Analysed test_samples from: 10%|█████▎ | 7/73 [00:03<00:38, 1.73it/s]
50.584333247894406 1
Analysed test_samples from: 11%|██████ | 8/73 [00:04<00:37, 1.72it/s]
51.531237662875014 1
Analysed test_samples from: 12%|██████▊ | 9/73 [00:05<00:37, 1.72it/s]
48.35283503525923 1
Analysed test_samples from: 14%|███████▍ | 10/73 [00:05<00:34, 1.81it/s]
50.8231913656234 1
Analysed test_samples from: 15%|████████▏ | 11/73 [00:06<00:34, 1.80it/s]
52.795969927190626 1
Analysed test_samples from: 16%|████████▉ | 12/73 [00:06<00:32, 1.85it/s]
52.60844401705262 1
Analysed test_samples from: 18%|█████████▌ | 13/73 [00:07<00:33, 1.80it/s]
48.038094835866474 1
Analysed test_samples from: 19%|██████████▎ | 14/73 [00:07<00:32, 1.82it/s]
47.9149661452117 1
Analysed test_samples from: 21%|███████████ | 15/73 [00:08<00:31, 1.84it/s]
50.35682268059995 1
Analysed test_samples from: 22%|███████████▊ | 16/73 [00:08<00:30, 1.84it/s]
48.06192443816771 1
Analysed test_samples from: 23%|████████████▌ | 17/73 [00:09<00:31, 1.80it/s]
47.80198805616523 1
Analysed test_samples from: 25%|█████████████▎ | 18/73 [00:09<00:31, 1.74it/s]
50.377542945109 1
Analysed test_samples from: 26%|██████████████ | 19/73 [00:10<00:29, 1.83it/s]
48.1062649930045 1
Analysed test_samples from: 27%|██████████████▊ | 20/73 [00:10<00:27, 1.90it/s]
47.68916216273482 1
Analysed test_samples from: 29%|███████████████▌ | 21/73 [00:11<00:26, 1.98it/s]
46.809490583939876 1
Analysed test_samples from: 30%|████████████████▎ | 22/73 [00:12<00:27, 1.86it/s]
47.282053067007226 1
Analysed test_samples from: 32%|█████████████████ | 23/73 [00:12<00:26, 1.85it/s]
48.11019670391504 1
Analysed test_samples from: 33%|█████████████████▊ | 24/73 [00:13<00:27, 1.80it/s]
45.60974249880543 1
Analysed test_samples from: 34%|██████████████████▍ | 25/73 [00:13<00:26, 1.81it/s]
45.11835084880572 1
Analysed test_samples from: 36%|███████████████████▏ | 26/73 [00:14<00:26, 1.77it/s]
48.764964464667656 1
Analysed test_samples from: 37%|███████████████████▉ | 27/73 [00:14<00:26, 1.75it/s]
48.3480409360308 1
Analysed test_samples from: 38%|████████████████████▋ | 28/73 [00:15<00:25, 1.73it/s]
42.35721770348719 1
Analysed test_samples from: 40%|█████████████████████▍ | 29/73 [00:16<00:24, 1.76it/s]
42.91240877713044 1
Analysed test_samples from: 41%|██████████████████████▏ | 30/73 [00:16<00:24, 1.73it/s]
39.99562467888545 1
Analysed test_samples from: 42%|██████████████████████▉ | 31/73 [00:17<00:25, 1.67it/s]
40.79568458935276 1
Analysed test_samples from: 44%|███████████████████████▋ | 32/73 [00:17<00:25, 1.62it/s]
41.05507556330633 1
Analysed test_samples from: 45%|████████████████████████▍ | 33/73 [00:18<00:24, 1.62it/s]
38.565262395078065 1
Analysed test_samples from: 47%|█████████████████████████▏ | 34/73 [00:19<00:24, 1.59it/s]
35.85114564862623 1
Analysed test_samples from: 48%|█████████████████████████▉ | 35/73 [00:19<00:22, 1.69it/s]
42.80283695005946 1
Analysed test_samples from: 49%|██████████████████████████▋ | 36/73 [00:20<00:22, 1.65it/s]
43.19136909051876 1
Analysed test_samples from: 51%|███████████████████████████▎ | 37/73 [00:20<00:20, 1.71it/s]
42.028631552756174 1
Analysed test_samples from: 52%|████████████████████████████ | 38/73 [00:21<00:21, 1.66it/s]
39.862868130405175 1
Analysed test_samples from: 53%|████████████████████████████▊ | 39/73 [00:22<00:20, 1.65it/s]
40.17253187111105 1
Analysed test_samples from: 55%|█████████████████████████████▌ | 40/73 [00:22<00:19, 1.73it/s]
37.73659180541854 1
Analysed test_samples from: 56%|██████████████████████████████▎ | 41/73 [00:23<00:19, 1.67it/s]
36.197727954228995 1
Analysed test_samples from: 58%|███████████████████████████████ | 42/73 [00:23<00:18, 1.66it/s]
37.49641609222998 1
Analysed test_samples from: 59%|███████████████████████████████▊ | 43/73 [00:24<00:18, 1.62it/s]
33.89044465582461 1
Analysed test_samples from: 60%|████████████████████████████████▌ | 44/73 [00:25<00:17, 1.63it/s]
35.37136292692365 1
Analysed test_samples from: 62%|█████████████████████████████████▎ | 45/73 [00:25<00:17, 1.60it/s]
37.3994050059425 1
Analysed test_samples from: 63%|██████████████████████████████████ | 46/73 [00:26<00:16, 1.62it/s]
36.690002015108654 1
Analysed test_samples from: 64%|██████████████████████████████████▊ | 47/73 [00:27<00:16, 1.61it/s]
34.611891339520895 1
Analysed test_samples from: 66%|███████████████████████████████████▌ | 48/73 [00:27<00:15, 1.61it/s]
36.75770357700703 1
Analysed test_samples from: 67%|████████████████████████████████████▏ | 49/73 [00:28<00:14, 1.60it/s]
38.31363742662252 1
Analysed test_samples from: 68%|████████████████████████████████████▉ | 50/73 [00:28<00:14, 1.56it/s]
36.23287649824114 1
Analysed test_samples from: 70%|█████████████████████████████████████▋ | 51/73 [00:29<00:13, 1.57it/s]
36.9766864363922 1
Analysed test_samples from: 71%|██████████████████████████████████████▍ | 52/73 [00:30<00:13, 1.57it/s]
39.770453380762355 1
Analysed test_samples from: 73%|███████████████████████████████████████▏ | 53/73 [00:30<00:12, 1.60it/s]
44.22975597266849 1
Analysed test_samples from: 74%|███████████████████████████████████████▉ | 54/73 [00:31<00:11, 1.64it/s]
43.472436072541356 1
Analysed test_samples from: 75%|████████████████████████████████████████▋ | 55/73 [00:32<00:11, 1.59it/s]
41.88065530699804 1
Analysed test_samples from: 77%|█████████████████████████████████████████▍ | 56/73 [00:32<00:10, 1.57it/s]
41.62159209658565 1
Analysed test_samples from: 78%|██████████████████████████████████████████▏ | 57/73 [00:33<00:10, 1.57it/s]
42.22356388029505 1
Analysed test_samples from: 79%|██████████████████████████████████████████▉ | 58/73 [00:33<00:09, 1.63it/s]
44.28822709207622 1
Analysed test_samples from: 81%|███████████████████████████████████████████▋ | 59/73 [00:34<00:08, 1.64it/s]
46.68435346016454 1
Analysed test_samples from: 82%|████████████████████████████████████████████▍ | 60/73 [00:35<00:07, 1.67it/s]
46.78740780002332 1
Analysed test_samples from: 84%|█████████████████████████████████████████████ | 61/73 [00:35<00:07, 1.70it/s]
46.34622359434884 1
Analysed test_samples from: 85%|█████████████████████████████████████████████▊ | 62/73 [00:36<00:06, 1.71it/s]
46.51023535554077 1
Analysed test_samples from: 86%|██████████████████████████████████████████████▌ | 63/73 [00:36<00:06, 1.63it/s]
45.66363432623487 1
Analysed test_samples from: 88%|███████████████████████████████████████████████▎ | 64/73 [00:37<00:05, 1.59it/s]
41.466159552473286 1
Analysed test_samples from: 89%|████████████████████████████████████████████████ | 65/73 [00:38<00:05, 1.56it/s]
39.83857804991645 1
Analysed test_samples from: 90%|████████████████████████████████████████████████▊ | 66/73 [00:38<00:04, 1.54it/s]
42.55665701951628 1
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47.20197101755791 1
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47.36750549220996 1
Analysed test_samples from: 95%|███████████████████████████████████████████████████ | 69/73 [00:40<00:02, 1.57it/s]
42.860908068939835 1
Analysed test_samples from: 96%|███████████████████████████████████████████████████▊ | 70/73 [00:41<00:01, 1.53it/s]
43.18316251521779 1
Analysed test_samples from: 97%|████████████████████████████████████████████████████▌ | 71/73 [00:42<00:01, 1.61it/s]
45.718376156288656 1
Analysed test_samples from: 99%|█████████████████████████████████████████████████████▎| 72/73 [00:42<00:00, 1.54it/s]
45.86117832245943 1
Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:43<00:00, 1.68it/s]
44.09246732174286 False False
Wiedeń model selection: 0%| | 0/27 [00:00<?, ?it/s]
(0, 0, 0) 2
Wiedeń model selection: 4%|██▏ | 1/27 [00:00<00:07, 3.52it/s]
1 Order: (0, 0, 0), AICc: 3903.44, ADF p-value: 0.0000, Best_Order: (0, 0, 0), Best_aicc: 3903.44 (0, 0, 1) 2
Wiedeń model selection: 7%|████▎ | 2/27 [00:00<00:07, 3.35it/s]
1 Order: (0, 0, 1), AICc: 3665.69, ADF p-value: 0.0000, Best_Order: (0, 0, 1), Best_aicc: 3665.69 (0, 0, 2) 2
Wiedeń model selection: 11%|██████▌ | 3/27 [00:00<00:06, 3.60it/s]
1 Order: (0, 0, 2), AICc: 3574.13, ADF p-value: 0.0000, Best_Order: (0, 0, 2), Best_aicc: 3574.13 (0, 1, 0) 2
Wiedeń model selection: 15%|████████▋ | 4/27 [00:01<00:05, 3.85it/s]
1 Order: (0, 1, 0), AICc: 3364.85, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3364.85 (0, 1, 1) 2 1
Wiedeń model selection: 19%|██████████▉ | 5/27 [00:01<00:05, 4.14it/s]
Order: (0, 1, 1), AICc: 3326.69, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3326.69 (0, 1, 2) 2
Wiedeń model selection: 22%|█████████████ | 6/27 [00:01<00:05, 3.91it/s]
1 Order: (0, 1, 2), AICc: 3281.35, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35 (0, 2, 0) 2
Wiedeń model selection: 26%|███████████████▎ | 7/27 [00:01<00:04, 4.07it/s]
1 Order: (0, 2, 0), AICc: 3879.09, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35 (0, 2, 1) 2
Wiedeń model selection: 30%|█████████████████▍ | 8/27 [00:02<00:05, 3.66it/s]
1 Order: (0, 2, 1), AICc: 3360.08, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35 (0, 2, 2) 2
Wiedeń model selection: 33%|███████████████████▋ | 9/27 [00:02<00:05, 3.44it/s]
1 Order: (0, 2, 2), AICc: 3325.23, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35 (1, 0, 0) 2
Wiedeń model selection: 37%|█████████████████████▍ | 10/27 [00:02<00:04, 3.47it/s]
1 Order: (1, 0, 0), AICc: 3369.36, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35 (1, 0, 1) 2
Wiedeń model selection: 41%|███████████████████████▋ | 11/27 [00:03<00:05, 2.90it/s]
1 Order: (1, 0, 1), AICc: 3332.45, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35 (1, 0, 2) 2
Wiedeń model selection: 44%|█████████████████████████▊ | 12/27 [00:03<00:06, 2.33it/s]
1 Order: (1, 0, 2), AICc: 3289.46, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35 (1, 1, 0) 2
Wiedeń model selection: 48%|███████████████████████████▉ | 13/27 [00:04<00:05, 2.75it/s]
1 Order: (1, 1, 0), AICc: 3350.85, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35 (1, 1, 1) 2
Wiedeń model selection: 52%|██████████████████████████████ | 14/27 [00:04<00:04, 2.67it/s]
1 Order: (1, 1, 1), AICc: 3278.62, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 3278.62 (1, 1, 2) 2
Wiedeń model selection: 56%|████████████████████████████████▏ | 15/27 [00:04<00:04, 2.65it/s]
1 Order: (1, 1, 2), AICc: 3272.46, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46 (1, 2, 0) 2
Wiedeń model selection: 59%|██████████████████████████████████▎ | 16/27 [00:05<00:03, 2.93it/s]
1 Order: (1, 2, 0), AICc: 3730.18, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46 (1, 2, 1) 2
Wiedeń model selection: 63%|████████████████████████████████████▌ | 17/27 [00:05<00:03, 2.59it/s]
1 Order: (1, 2, 1), AICc: 3347.19, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46 (1, 2, 2) 2
Wiedeń model selection: 67%|██████████████████████████████████████▋ | 18/27 [00:06<00:04, 1.93it/s]
1 Order: (1, 2, 2), AICc: 3280.75, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46 (2, 0, 0) 2
Wiedeń model selection: 70%|████████████████████████████████████████▊ | 19/27 [00:06<00:03, 2.03it/s]
1 Order: (2, 0, 0), AICc: 3351.08, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46 (2, 0, 1) 2
Wiedeń model selection: 74%|██████████████████████████████████████████▉ | 20/27 [00:07<00:03, 1.97it/s]
1 Order: (2, 0, 1), AICc: 3285.47, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46 (2, 0, 2) 2
Wiedeń model selection: 78%|█████████████████████████████████████████████ | 21/27 [00:08<00:03, 1.78it/s]
1 Order: (2, 0, 2), AICc: 3281.86, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46 (2, 1, 0) 2
Wiedeń model selection: 81%|███████████████████████████████████████████████▎ | 22/27 [00:08<00:02, 2.16it/s]
1 Order: (2, 1, 0), AICc: 3315.59, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46 (2, 1, 1) 2
Wiedeń model selection: 85%|█████████████████████████████████████████████████▍ | 23/27 [00:08<00:01, 2.34it/s]
1 Order: (2, 1, 1), AICc: 3280.16, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46 (2, 1, 2) 2
Wiedeń model selection: 89%|███████████████████████████████████████████████████▌ | 24/27 [00:09<00:01, 2.04it/s]
1 Order: (2, 1, 2), AICc: 3274.09, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46 (2, 2, 0) 2
Wiedeń model selection: 93%|█████████████████████████████████████████████████████▋ | 25/27 [00:09<00:00, 2.26it/s]
1 Order: (2, 2, 0), AICc: 3620.57, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46 (2, 2, 1) 2
Wiedeń model selection: 96%|███████████████████████████████████████████████████████▊ | 26/27 [00:10<00:00, 2.26it/s]
1 Order: (2, 2, 1), AICc: 3320.09, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46 (2, 2, 2) 2
Wiedeń model selection: 100%|██████████████████████████████████████████████████████████| 27/27 [00:10<00:00, 2.47it/s]
1 Order: (2, 2, 2), AICc: 3289.88, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
Analysed test_samples from: 0%| | 0/73 [00:00<?, ?it/s]
1
Analysed test_samples from: 1%|▊ | 1/73 [00:00<00:25, 2.86it/s]
57.8449434787823 1
Analysed test_samples from: 3%|█▌ | 2/73 [00:00<00:27, 2.59it/s]
62.683339461989114 1
Analysed test_samples from: 4%|██▎ | 3/73 [00:01<00:31, 2.19it/s]
59.37077225819115 1
Analysed test_samples from: 5%|███ | 4/73 [00:01<00:30, 2.27it/s]
57.74631227123143 1
Analysed test_samples from: 7%|███▊ | 5/73 [00:02<00:28, 2.42it/s]
56.33440335380239 1
Analysed test_samples from: 8%|████▌ | 6/73 [00:02<00:26, 2.50it/s]
59.857299010301865 1
Analysed test_samples from: 10%|█████▎ | 7/73 [00:02<00:27, 2.44it/s]
59.6887483008113 1
Analysed test_samples from: 11%|██████ | 8/73 [00:03<00:27, 2.40it/s]
57.03954861785948 1
Analysed test_samples from: 12%|██████▊ | 9/73 [00:03<00:27, 2.34it/s]
57.13819755294368 1
Analysed test_samples from: 14%|███████▍ | 10/73 [00:04<00:26, 2.38it/s]
56.05899567526647 1
Analysed test_samples from: 15%|████████▏ | 11/73 [00:04<00:26, 2.33it/s]
55.76440588514446 1
Analysed test_samples from: 16%|████████▉ | 12/73 [00:05<00:26, 2.30it/s]
56.43013013328056 1
Analysed test_samples from: 18%|█████████▌ | 13/73 [00:05<00:26, 2.25it/s]
57.124519123091474 1
Analysed test_samples from: 19%|██████████▎ | 14/73 [00:05<00:25, 2.31it/s]
57.14801958011789 1
Analysed test_samples from: 21%|███████████ | 15/73 [00:06<00:24, 2.35it/s]
51.9351261623211 1
Analysed test_samples from: 22%|███████████▊ | 16/73 [00:06<00:23, 2.46it/s]
54.57032860227861 1
Analysed test_samples from: 23%|████████████▌ | 17/73 [00:07<00:22, 2.49it/s]
55.3991129310446 1
Analysed test_samples from: 25%|█████████████▎ | 18/73 [00:07<00:22, 2.43it/s]
55.88986234021294 1
Analysed test_samples from: 26%|██████████████ | 19/73 [00:07<00:22, 2.42it/s]
55.74093893652491 1
Analysed test_samples from: 27%|██████████████▊ | 20/73 [00:08<00:21, 2.42it/s]
53.059578050800454 1
Analysed test_samples from: 29%|███████████████▌ | 21/73 [00:08<00:21, 2.47it/s]
50.26464816993388 1
Analysed test_samples from: 30%|████████████████▎ | 22/73 [00:09<00:20, 2.52it/s]
51.2548956655222 1
Analysed test_samples from: 32%|█████████████████ | 23/73 [00:09<00:19, 2.53it/s]
52.054519492912995 1
Analysed test_samples from: 33%|█████████████████▊ | 24/73 [00:09<00:19, 2.48it/s]
50.928670588228 1
Analysed test_samples from: 34%|██████████████████▍ | 25/73 [00:10<00:19, 2.42it/s]
49.84766431215074 1
Analysed test_samples from: 36%|███████████████████▏ | 26/73 [00:10<00:18, 2.48it/s]
56.75859612369776 1
Analysed test_samples from: 37%|███████████████████▉ | 27/73 [00:11<00:18, 2.43it/s]
55.264319793396794 1
Analysed test_samples from: 38%|████████████████████▋ | 28/73 [00:11<00:18, 2.43it/s]
54.210523093821294 1
Analysed test_samples from: 40%|█████████████████████▍ | 29/73 [00:12<00:18, 2.44it/s]
49.359766857082334 1
Analysed test_samples from: 41%|██████████████████████▏ | 30/73 [00:12<00:17, 2.42it/s]
48.85940792257731 1
Analysed test_samples from: 42%|██████████████████████▉ | 31/73 [00:12<00:17, 2.42it/s]
46.72366396670721 1
Analysed test_samples from: 44%|███████████████████████▋ | 32/73 [00:13<00:17, 2.40it/s]
54.393783452209746 1
Analysed test_samples from: 45%|████████████████████████▍ | 33/73 [00:13<00:16, 2.42it/s]
52.740114124020806 1
Analysed test_samples from: 47%|█████████████████████████▏ | 34/73 [00:14<00:16, 2.42it/s]
45.756286240857804 1
Analysed test_samples from: 48%|█████████████████████████▉ | 35/73 [00:14<00:15, 2.44it/s]
46.909222072419226 1
Analysed test_samples from: 49%|██████████████████████████▋ | 36/73 [00:14<00:15, 2.45it/s]
49.347706712951805 1
Analysed test_samples from: 51%|███████████████████████████▎ | 37/73 [00:15<00:14, 2.44it/s]
44.11684142456111 1
Analysed test_samples from: 52%|████████████████████████████ | 38/73 [00:15<00:14, 2.48it/s]
44.49814772830541 1
Analysed test_samples from: 53%|████████████████████████████▊ | 39/73 [00:16<00:13, 2.50it/s]
48.024888826862636 1
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44.924229242657 1
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41.50723892255159 1
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41.64491860788039 1
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41.722504603576965 1
Analysed test_samples from: 60%|████████████████████████████████▌ | 44/73 [00:18<00:12, 2.31it/s]
40.34241618016443 1
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40.108723593561294 1
Analysed test_samples from: 63%|██████████████████████████████████ | 46/73 [00:19<00:12, 2.22it/s]
36.81154899469463 1
Analysed test_samples from: 64%|██████████████████████████████████▊ | 47/73 [00:19<00:11, 2.30it/s]
39.65189657134642 1
Analysed test_samples from: 66%|███████████████████████████████████▌ | 48/73 [00:20<00:11, 2.25it/s]
41.514490074811846 1
Analysed test_samples from: 67%|████████████████████████████████████▏ | 49/73 [00:20<00:10, 2.26it/s]
43.89157527070361 1
Analysed test_samples from: 68%|████████████████████████████████████▉ | 50/73 [00:20<00:09, 2.33it/s]
40.91405640738739 1
Analysed test_samples from: 70%|█████████████████████████████████████▋ | 51/73 [00:21<00:09, 2.38it/s]
41.317910338500816 1
Analysed test_samples from: 71%|██████████████████████████████████████▍ | 52/73 [00:21<00:08, 2.43it/s]
45.459669397172355 1
Analysed test_samples from: 73%|███████████████████████████████████████▏ | 53/73 [00:22<00:08, 2.34it/s]
45.323642911167845 1
Analysed test_samples from: 74%|███████████████████████████████████████▉ | 54/73 [00:22<00:08, 2.27it/s]
51.51232115170022 1
Analysed test_samples from: 75%|████████████████████████████████████████▋ | 55/73 [00:23<00:07, 2.31it/s]
47.18462437502026 1
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50.60343242112565 1
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44.4414412699819 False False
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from pmdarima import auto_arima
from pmdarima import auto_arima
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from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm
def create_lag_features(df, lags, target_col='TEMP'):
for lag in lags:
df[f'{target_col}_lag{lag}'] = df[target_col].shift(lag)
return df
# Config
lags = [1, 2, 3,7] # example lags
exog_vars = ['DEWP', 'Month', 'PRCP', 'VISIB']
all_features = [f'TEMP_lag{l}' for l in lags] + exog_vars
forecast_results = []
for city, df in city_dataframes.items():
data = df.copy()
data.reset_index(inplace=True)
data['DATE'] = pd.to_datetime(data['DATE'], errors='coerce')
data['Month'] = data['DATE'].dt.month
data.set_index('DATE', inplace=True)
# Filter last 2 years
cutoff_date = data.index.max() - pd.DateOffset(years=2)
data = data[data.index >= cutoff_date]
data[exog_vars + ['TEMP']] = data[exog_vars + ['TEMP']].apply(pd.to_numeric, errors='coerce')
data = create_lag_features(data, lags)
data.dropna(inplace=True)
# Split
n_total = len(data)
n_test = int(0.1 * n_total)
n_val = int(0.15 * (n_total - n_test))
n_train = n_total - n_val - n_test
train = data.iloc[:n_train]
val = data.iloc[n_train:n_train + n_val]
test = data.iloc[n_train + n_val:]
combined = pd.concat([train, val])
# predictions, actuals = [], []
predictions, lowers, uppers, actuals = [], [], [], []
for t in tqdm(range(len(test)), desc=f"Rolling RF for {city}"):
try:
# Define X and y for model training
X_train = combined[all_features]
y_train = combined['TEMP']
# Fit model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Prepare test sample
x_next = test[all_features].iloc[[t]]
y_next = test['TEMP'].iloc[t]
# y_pred = model.predict(x_next)[0]
all_preds = np.array([tree.predict(x_next)[0] for tree in model.estimators_])
pred_mean = np.mean(all_preds)
ci_lower = np.percentile(all_preds, 2.5)
ci_upper = np.percentile(all_preds, 97.5)
# predictions.append(y_pred)
predictions.append(pred_mean)
lowers.append(ci_lower)
uppers.append(ci_upper)
actuals.append(y_next)
# Add row to combined history (simulate rolling)
next_row = test.iloc[[t]].copy()
next_row['TEMP'] = pred_mean # use predicted value
combined = pd.concat([combined, next_row])
except Exception as e:
print(f"{city} step {t} failed: {e}")
predictions.append(np.nan)
actuals.append(test['TEMP'].iloc[t])
# Evaluation
y_true = np.array(actuals)
y_pred = np.array(predictions)
mask = ~np.isnan(y_pred)
if mask.sum() == 0:
print(f"{city} — no valid predictions")
continue
mae = mean_absolute_error(y_true[mask], y_pred[mask])
rmse = np.sqrt(mean_squared_error(y_true[mask], y_pred[mask]))
forecast_results.append({
'City': city,
'Model': 'RandomForest',
'Test MAE': mae,
'Test RMSE': rmse
})
# Plot
test_index = test.index[:len(y_pred)]
plt.figure(figsize=(15, 6))
plt.plot(data.index[:n_train], data['TEMP'].iloc[:n_train], label='Train', color='blue')
plt.plot(data.index[n_train:n_train+n_val], data['TEMP'].iloc[n_train:n_train+n_val], label='Val', color='orange')
plt.plot(test_index, y_true, label='Test Actual', color='green')
plt.fill_between(test_index, lowers, uppers, color='red', alpha=0.3, label='95% CI')
plt.plot(test_index, y_pred, label='Forecast (RF)', color='red')
plt.title(f"Random Forest Rolling Forecast — {city}")
plt.xlabel("Date")
plt.ylabel("Temperature")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.savefig(f"../../plots/{city}_randomforest.png")
plt.close()
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm
def create_lag_features(df, lags, target_col='TEMP'):
for lag in lags:
df[f'{target_col}_lag{lag}'] = df[target_col].shift(lag)
return df
# Config
lags = [1, 2, 3,7] # example lags
exog_vars = ['DEWP', 'Month', 'PRCP', 'VISIB']
all_features = [f'TEMP_lag{l}' for l in lags] + exog_vars
forecast_results = []
for city, df in city_dataframes.items():
data = df.copy()
data.reset_index(inplace=True)
data['DATE'] = pd.to_datetime(data['DATE'], errors='coerce')
data['Month'] = data['DATE'].dt.month
data.set_index('DATE', inplace=True)
# Filter last 2 years
cutoff_date = data.index.max() - pd.DateOffset(years=2)
data = data[data.index >= cutoff_date]
data[exog_vars + ['TEMP']] = data[exog_vars + ['TEMP']].apply(pd.to_numeric, errors='coerce')
data = create_lag_features(data, lags)
data.dropna(inplace=True)
# Split
n_total = len(data)
n_test = int(0.1 * n_total)
n_val = int(0.15 * (n_total - n_test))
n_train = n_total - n_val - n_test
train = data.iloc[:n_train]
val = data.iloc[n_train:n_train + n_val]
test = data.iloc[n_train + n_val:]
combined = pd.concat([train, val])
# predictions, actuals = [], []
predictions, lowers, uppers, actuals = [], [], [], []
for t in tqdm(range(len(test)), desc=f"Rolling RF for {city}"):
try:
# Define X and y for model training
X_train = combined[all_features]
y_train = combined['TEMP']
# Fit model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Prepare test sample
x_next = test[all_features].iloc[[t]]
y_next = test['TEMP'].iloc[t]
# y_pred = model.predict(x_next)[0]
all_preds = np.array([tree.predict(x_next)[0] for tree in model.estimators_])
pred_mean = np.mean(all_preds)
ci_lower = np.percentile(all_preds, 2.5)
ci_upper = np.percentile(all_preds, 97.5)
# predictions.append(y_pred)
predictions.append(pred_mean)
lowers.append(ci_lower)
uppers.append(ci_upper)
actuals.append(y_next)
# Add row to combined history (simulate rolling)
next_row = test.iloc[[t]].copy()
next_row['TEMP'] = pred_mean # use predicted value
combined = pd.concat([combined, next_row])
except Exception as e:
print(f"{city} step {t} failed: {e}")
predictions.append(np.nan)
actuals.append(test['TEMP'].iloc[t])
# Evaluation
y_true = np.array(actuals)
y_pred = np.array(predictions)
mask = ~np.isnan(y_pred)
if mask.sum() == 0:
print(f"{city} — no valid predictions")
continue
mae = mean_absolute_error(y_true[mask], y_pred[mask])
rmse = np.sqrt(mean_squared_error(y_true[mask], y_pred[mask]))
forecast_results.append({
'City': city,
'Model': 'RandomForest',
'Test MAE': mae,
'Test RMSE': rmse
})
# Plot
test_index = test.index[:len(y_pred)]
plt.figure(figsize=(15, 6))
plt.plot(data.index[:n_train], data['TEMP'].iloc[:n_train], label='Train', color='blue')
plt.plot(data.index[n_train:n_train+n_val], data['TEMP'].iloc[n_train:n_train+n_val], label='Val', color='orange')
plt.plot(test_index, y_true, label='Test Actual', color='green')
plt.fill_between(test_index, lowers, uppers, color='red', alpha=0.3, label='95% CI')
plt.plot(test_index, y_pred, label='Forecast (RF)', color='red')
plt.title(f"Random Forest Rolling Forecast — {city}")
plt.xlabel("Date")
plt.ylabel("Temperature")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.savefig(f"../../plots/{city}_randomforest.png")
plt.close()
Rolling RF for Ateny: 100%|████████████████████████████████████████████████████████████| 72/72 [00:23<00:00, 3.01it/s] Rolling RF for Berlin: 100%|███████████████████████████████████████████████████████████| 72/72 [00:24<00:00, 2.90it/s] Rolling RF for Bruksela: 100%|█████████████████████████████████████████████████████████| 72/72 [00:25<00:00, 2.79it/s] Rolling RF for Budapeszt: 100%|████████████████████████████████████████████████████████| 72/72 [00:24<00:00, 2.96it/s] Rolling RF for Lisbona: 100%|██████████████████████████████████████████████████████████| 69/69 [00:22<00:00, 3.09it/s] Rolling RF for Londyn: 100%|███████████████████████████████████████████████████████████| 71/71 [00:28<00:00, 2.52it/s] Rolling RF for Madryd: 100%|███████████████████████████████████████████████████████████| 72/72 [00:25<00:00, 2.86it/s] Rolling RF for Moskwa: 100%|███████████████████████████████████████████████████████████| 28/28 [00:06<00:00, 4.61it/s] Rolling RF for Paryż: 100%|████████████████████████████████████████████████████████████| 72/72 [00:25<00:00, 2.79it/s] Rolling RF for Praga: 100%|████████████████████████████████████████████████████████████| 72/72 [00:26<00:00, 2.77it/s] Rolling RF for Warszawa: 100%|█████████████████████████████████████████████████████████| 72/72 [00:25<00:00, 2.80it/s] Rolling RF for Wiedeń: 100%|███████████████████████████████████████████████████████████| 72/72 [00:26<00:00, 2.76it/s]
In [13]:
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forecast_results
forecast_results
Out[13]:
[{'City': 'Ateny',
'Model': 'RandomForest',
'Test MAE': 2.5913532538431117,
'Test RMSE': 3.3110576437363473},
{'City': 'Berlin',
'Model': 'RandomForest',
'Test MAE': 2.213854448364975,
'Test RMSE': 2.763150691958107},
{'City': 'Bruksela',
'Model': 'RandomForest',
'Test MAE': 2.104675499193953,
'Test RMSE': 2.5358631943032495},
{'City': 'Budapeszt',
'Model': 'RandomForest',
'Test MAE': 2.975801361490591,
'Test RMSE': 3.5485013812483865},
{'City': 'Lisbona',
'Model': 'RandomForest',
'Test MAE': 1.8826458219314957,
'Test RMSE': 2.5379073770421536},
{'City': 'Londyn',
'Model': 'RandomForest',
'Test MAE': 1.613961442661724,
'Test RMSE': 1.9668790687150095},
{'City': 'Madryd',
'Model': 'RandomForest',
'Test MAE': 2.404226537265454,
'Test RMSE': 2.965278670613778},
{'City': 'Moskwa',
'Model': 'RandomForest',
'Test MAE': 3.6079551258404217,
'Test RMSE': 4.908083692679886},
{'City': 'Paryż',
'Model': 'RandomForest',
'Test MAE': 2.098009928791851,
'Test RMSE': 2.5611668885396877},
{'City': 'Praga',
'Model': 'RandomForest',
'Test MAE': 2.0448051753992456,
'Test RMSE': 2.687780845547912},
{'City': 'Warszawa',
'Model': 'RandomForest',
'Test MAE': 2.1570712354846355,
'Test RMSE': 2.584586108583517},
{'City': 'Wiedeń',
'Model': 'RandomForest',
'Test MAE': 2.391423579789416,
'Test RMSE': 3.075091837920412}]